Objective: To develop a 24 hour physical activity index for school aged children, and to analyze the association of 24 hour physical activity index with screening myopia and obesity, so as to provide a more effective assessment tool for student health risk screening. Methods: From April to June 2024, a total of 451 students in Grades 3-4 from two monitored primary schools in Shanghai were selected using stratified cluster random sampling method. Data on eight core indicators, including daily moderate to vigorous physical activity time, total physical activity time, outdoor activity time, screen time, sleep duration, sleep efficiency, social jetlag and daytime sleepiness, were collected through questionnaires and accelerometer monitoring. Each indicator was standardized and synthesized into a 0-80 point school aged children s 24 hour physical activity index. Spearman correlation analysis and t-test were used to assess consistency between questionnaire and accelerometer derived indices. Multivariate Logistic regression was applied to analyze the association of strength of the composite index with single behavior indicators in screening myopia and overweight/obesity. Results: The compliance rates were higher for moderate to vigorous physical activity time and screen time (50.8%, 98.7%), while the compliance rate for outdoor activity time was only 42.6%, and that for sufficient sleep duration was merely 10.2%. There was no significant difference between the total scores derived from the questionnaire and accelerometer methods (45.13±5.83, 45.05±6.87, t=0.29, P >0.05), but they showed a strong positive correlation ( r=0.58, P <0.01). Multivariate Logistic regression revealed that adjusting for individual behaviors such as grade, gender and both parents being myopic, among single behavior indicators of 24 hour physical activity index, only insufficient outdoor activity time was significantly associated with screening myopia among school aged children ( OR=1.50, 95%CI =1.01-2.21); the detection risk of screening myopia and obesity in the low index group were higher than those in the high index group ( OR=2.47, 95%CI =1.02-5.96; OR=16.63,95%CI = 5.99- 46.20) (all P <0.05). Conclusion The developed 24 hour physical activity index for school aged children demonstrates good measurement accuracy and shows stronger associations with screening myopia and obesity than single behavior indicator.

ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

Objective: To analyze the current burden of autism spectrum disorder (ASD) among children and adolescents in China and globally, and to predict the disease burden from 2024 to 2035, providing a scientific basis for formulating relevant public health policies and intervention measures. Methods: Based on the Global Burden of Disease (GBD) database in 2023, the Joinpoint regression model was used to analyze the changing trends of the disease burden of ASD among children and adolescents in China and globally from 1990 to 2023, and the average annual percent change (AAPC) was calculated. An autoregressive integrated moving average (ARIMA) model was constructed to predict the disease burden trends of ASD among children and adolescents in China and globally from 2024 to 2035. Results: The prevalence and disability adjusted life years (DALYs) rate of ASD among children and adolescents in China increased from 452.69/100 000 and 86.67/100 000 in 1990 to 762.84/100 000 and 148.52/ 100 000 in 2023(AAPC=1.60%, 1.65%, both P <0.01). The prevalence and DALYs rate of ASD among children and adolescents globally increased from 648.49/100 000 and 123.47/100 000 to 862.44/100 000 and 167.16/100 000(AAPC=0.87%, 0.93%, both P <0.01). In 2023, the highest ASD prevalence and DALY rates occurred in children under 5 years old, with China reporting 848.14/100 000 and 166.69/100 000, both below the global averages of 928.80/100 000 and 181.34/100 000. Projections indicated that by 2035, the ASD prevalence and DALY rates in China would rise to 906.83/100 000 and 168.71/100 000, still below the global averages of 938.04/100 000 and 184.49/100 000. Conclusion The disease burden of ASD among children and adolescents in China and globally has generally increased from 1990 to 2023, with a higher risk of disease at younger ages.

BackgroundPain is one of the most common physical symptoms in patients with major depressive disorder （MDD）. The high comorbidity rate of MDD and pain aggravates patients' physical and psychological distress， undermines their treatment adherence， and contributes to poor clinical outcomes， posing a critical challenge for the clinical management of MDD. Anxiety symptoms are strongly associated with the pain severity in patients with MDD， yet the underlying mechanisms of the association remain poorly elucidated. Accumulating evidence have indicated that depressive symptoms and pain vigilance and awareness are involved in the modulation of pain perception. Nevertheless， few studies have incorporated these variables into an integrated analytical framework to clarify their parallel mediating effects on the relationship between anxiety symptoms and pain severity. ObjectiveTo explore the mediating pathways linking anxiety symptoms to pain severity via depressive symptoms and pain vigilance and awareness in patients with MDD， so as to provide insights for precise intervention for MDD accompanied by pain symptoms. MethodsA total of 249 patients who met the diagnostic criteria for MDD specified in the Diagnostic and Statistical Manual of Mental Disorders， fifth edition （DSM-5） were recruited from Beijing Huilongguan Hospital from May to September 2025. Hamilton Depression Scale-17 item （HAMD-17）， Hamilton Anxiety Scale （HAMA）， Pain Vigilance and Awareness Questionnaire （PVAQ） and Numerical Rating Scale （NRS） were used for assessment. Partial correlation analyses were performed to examine correlations among these variables. Parallel mediating effects were tested using model 4 of the Process macro for SPSS， with depressive symptoms and pain vigilance and awareness as parallel mediators linking anxiety symptoms to pain severity. ResultsA total of 135 patients （54.22%） reported pain， comprising 59 （43.70%） with mild pain， 43 （31.85%） with moderate pain， and 33 （24.45%） with severe pain. Partial correlation analyses revealed significant positive correlations among HAMD-17， HAMA， PVAQ， and NRS scores in patients with MDD （r=0.198–0.424， P<0.05 or 0.01）. Anxiety symptoms positively predicted pain severity， with a direct effect of 0.128 （95% CI： 0.065–0.190）. Both depressive symptoms and pain vigilance and awareness acted as partial mediators linking anxiety symptoms to pain severity. The indirect effects were 0.057 （95% CI： 0.029–0.089） and 0.042 （95% CI： 0.016–0.079）， accounting for 25.22% and 18.58% of the total effect， respectively. ConclusionIn patients with MDD， anxiety symptoms exert a positive predictive effect on pain severity， which is partially mediated through the parallel roles of depressive symptoms and pain vigilance and awareness.

Objective: To establish a prediction model for high-risk cardiovascular disease (CVD) among residents aged 35 to 75 years, so as to provide the basis for improving CVD prevention and control measures. Methods: Permanent residents aged 35 to 75 years were selected from Dongcheng District, Beijing Municipality using the stratified random sampling method from 2018 to 2023. Demographic information, lifestyle, waist circumference and blood biochemical indicators were collected through questionnaire surveys, physical examinations and laboratory tests. Influencing factors for high-risk CVD among residents aged 35 to 75 years were identified using a multivariable logistic regression model, and a prediction model for high-risk CVD was established. The predictive effect was evaluated using the receiver operating characteristic (ROC) curve. Results: A total of 6 968 individuals were surveyed, including 2 821 males (40.49%) and 4 147 females (59.51%), and had a mean age of (59.92±9.33) years. There were 1 155 high-risk CVD population, with a detection rate of 16.58%. Multivariable logistic regression analysis showed that gender, age, smoking, central obesity, systolic blood pressure, fasting blood glucose, triglyceride and low-density lipoprotein cholesterol were influencing factors for high-risk CVD among residents aged 35 to 75 years (all P<0.05). The area under the ROC curve of the established prediction model was 0.849 (95%CI: 0.834-0.863), with a sensitivity of 0.693 and a specificity of 0.863, indicating good discrimination. Conclusion The model constructed by eight factors including demographic characteristics, lifestyle and blood biochemical indicators has good predictive value for high-risk CVD among residents aged 35 to 75 years.

Objective To discuss the application value of CT and DSA multimodal image fusion technology in endovascular interventional therapy for arterial occlusive lesions of lower extremities and to evaluate its efficacy and safety so as to provide a scientific basis for clinical decision-making.Methods A total of 283 lower limbs with arterial complete occlusive lesions,who received treatment at Affiliated Baiyun Hospital of Guizhou Medical University hospital from January 2020 to December 2023,were selected for this study.The 283 diseased lower limbs were randomly divided into study group(n=142)and control group(n=141).In the study group the endovascular interventional therapy assisted by CT and DSA multimodal image fusion technology was adopted,while in the control group the traditional DSA-guided endovascular interventional therapy was employed.The imaging parameters,surgical success rates,X-ray exposure doses,time spent for operation,incidence of postoperative complications,changes of ankle-brachial index(ABI),primary patency rate,assisted primary patency rate,and secondary patency rate were compared between the two groups.Results The surgical success rate in the study group was 96.47％,which was significantly higher than 87.94％in the control group(P＜0.05).The mean time spent for operation in the study group was(125.42±23.74)minutes,which was shorter than(147.81±29.33)minutes in the control group.The mean X-ray exposure dose in the study group was(2 856.34±427.82)mGy·cm2,which was lower than(3 674.53±512.60)mGy·cm2 in the control group.The incidence of postoperative complications in the study group was 4.23％,which was significantly lower than 12.57％in the control group(P＜0.05).The ABI values of the affected limbs in the study group and control group increased from preoperative(0.65±0.15)and(0.60±0.18)respectively to postoperative(1.09±0.32)and(0.90±0.28)respectively.The postoperative ABI value in the study group was higher than that in the control group(P＜0.05).The postoperative 12-month primary patency rate,assisted primary patency rate and secondary patency rate in the study group were 78.17％,85.92％and 90.14％respectively,which were better than 67.38％,75.89％and 80.85％respectively in the control group.Conclusion For arterial occlusive lesions of lower extremities,endovascular interventional therapy with the help of CT and DSA multimodal image fusion technology has high surgical success rates,low incidence of complications,and satisfactory revascularization rate.This technology provides new idea and method for the treatment of arterial occlusive lesions of lower extremities with high clinical safety.Therefore,this technology is worthy of clinical promotion and application.

Objective:To investigate the effect of CT-derived fractional flow reserve (CT-FFR) measurement sites on the values and the diagnostic performance, and to determine the optimal measurement site for CT-FFR using invasive FFR as the reference standard.Methods:This study was part of the CT-FFR CHINA clinical trial. Patients with suspected coronary artery disease who were scheduled for invasive coronary angiography (ICA) were prospectively recruited from five clinical centers across the country from November 2018 to March 2020. Each enrolled patient underwent coronary CT angiography (CCTA), CT-FFR, ICA, and invasive pressure wire-based FFR assessments sequentially within one week. Four groups of CT-FFR values were obtained on each enrolled target vessels according to different CT-FFR measurement locations: 1, 2, 3 cm distal to the target lesion, and terminal vessel groups. Spearman and Bland-Altman analyses were used to explore the correlation and consistency of CT-FFR values and FFR values at different measurement sites. The measurement deviation of CT-FFR was also compared. Diagnostic accuracy and performance of CT-FFR, including sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC), in discriminating myocardial ischemia were analyzed across all measurement site groups on a per-vessel level, using FFR as the reference standard.Results:A total of 289 patients with 345 target lesion vessels were included. According to CCTA, there were 51 target vessels (14.8%) with<50% stenosis, 106 vessels (30.7%) with 50%-69% stenosis, and 188 vessels (54.5%) with stenosis≥70%. At per-vessel level, CT-FFR and FFR values at each measurement position group were highly positively correlated: 1 cm distal to target lesion group, r=0.734 ( P<0.001); 2 cm distal to target lesion group, r=0.732 ( P<0.001); 3 cm distal to target lesion group, r=0.737 ( P<0.001); terminal vessel group was 0.719 ( P<0.001). At per-vessel level, CT-FFR and FFR values of all measurement sites were in good agreement (Bland-Altman analysis results): 1 cm distal to target lesion group, 0.014 (95% LoA 0.002-0.026); 2 cm distal to target lesion group, 0.026 (95% LoA 0.015-0.038); 3 cm distal to target lesion group, 0.040 (95% LoA 0.039-0.051); terminal vessel group, 0.075 (95% LoA 0.064-0.087). And at per-vessel level, the accuracy of diagnosing myocardial ischemia with CT-FFR at 1 cm was highest [84.6% (95% CI 80.4%-88.3%)], and the lowest accuracy in the terminal vessel group [67.0% (95% CI 61.7%-72.0%)]. However, there was no significant difference in the diagnostic accuracy of CT-FFR at 1 cm, 2 cm [80.6% (95% CI 76.1%-84.6%)] and 3 cm [77.5% (95% CI 72.6%-81.7%)]. AUC of CT-FFR at 1 cm distal to the lesion were both highest for global level and moderately stenosis (50%-69%) lesions [0.85 (95% CI 0.81-0.89), 0.84 (95% CI 0.77-0.90)]. And the differences were statistically significant among the four measurement location groups (all P<0.05). Conclusions:The deviation of CT-FFR increases with measurement site distance distal to target lesions. One centimeter distal to the target lesion is the optimal measurement site, and the CT-FFR value here shows the highest diagnostic performance for myocardial ischemic lesions, especially for moderate stenosis.

Objective To investigate the diagnostic accuracy and clinical applicability of the Linknet-VGG16 deep learning algorithm for measuring the effective optical zone(EOZ)after corneal refractive surgery.Methods This single-center retrospective cohort study included 69 patients(69 eyes)who underwent femtosecond laser-assisted in situ kerato-mileusis(FS-LASIK)(34 eyes)or small incision lenticule extraction(SMILE)(35 eyes)at the Refractive Surgery Center of Affiliated Zhengzhou Aier Eye Hospital of Henan University from June 2023 to June 2024.Data from the right eyes of all patients were selected for statistical analysis.During the surgery,patients in the FS-LASIK group adopted the VisuMax fem-tosecond laser system combined with the Amaris 750S excimer laser system,while those in the SMILE group only used the VisuMax femtosecond laser system.A total of 276 Pentacam images were re-examined postoperatively.A Linknet segmenta-tion model based on the VGG16 encoder was constructed,and image normalization techniques were applied to accelerate model convergence.Model performance was assessed using accuracy,intersection over union(IoU),and the Dice coeffi-cient.The traditional EOZ measurement method based on corneal tangential curvature served as the reference standard.Bland-Altman analysis was conducted to evaluate consistency across all images and within each group,and the time effi-ciency of both methods was compared.Results Six representative medical image segmentation architectures(U-Net,U-Net++,DeepLabv3-ResNet50,DeepLabv3+-ResNet50,Unet-Densenet169,and Linknet-VGG16)were systematically evaluated.The Linknet-VGG16 model demonstrated superior performance over the other 5 models in pixel-level accuracy,IoU and Dice coefficient,which were 99.83％,99.48％and 99.74％,respectively.Although there was no significant differ-ence in accuracy and Dice coefficient between Linknet-VGG16 and U-Net models(whose accuracy was 99.82％and Dice coefficient was 99.72％),the inference speed of the U-Net model(62.46 ms)was 31.76％slower than that of the Linknet-VGG16 model(42.62 ms).The evaluation results of a clinically applicable comprehensive scoring model(weights:accura-cy 20％,IoU 20％,Dice coefficient 20％,speed 25％,model size 15％)showed that the Linknet-VGG16 model achieved a score of 88.01,surpassing other architectures(U-Net:86.29;DeepLabv3+-ResNet50:80.41;DeepLabv3-ResNet50:73.82;U-Net++:73.22;Unet-Densenet169:66.66).Bland-Altman analysis revealed that the mean difference of the 136 images in the FS-LASIK group was 0.01 mm[95％limits of agreement(LoA):-0.36 to 0.35 mm],with 96.3％of data points falling within the LoA.The mean difference of the 140 images in the SMILE group was-0.01 mm(95％LoA:-0.36 to 0.33 mum),with 95.7％of data points falling within the LoA.The mean difference of all 276 images was 0.00 mm(95％LoA:-0.36 to 0.34 mm),with 96.4％of data points falling within the LoA.These results indicated excellent consistency.The average measurement time per image using the traditional EOZ measurement method was 13.00 minutes,whereas the deep learning model required only 3.22 seconds.Conclusion The traditional EOZ measurement method based on corne-al tangential curvature exhibits good consistency with the fully automatic EOZ measurement method based on deep learning algorithms,achieving high image recognition accuracy.Additionally,the deep learning algorithm significantly reduces measurement time,compared with the traditional method based on corneal tangential curvature.

Objective:To study the role of cadherin 1 (CDH1) gene DNA methylation in autoimmune thyroiditis (AIT) in population from different water-iodine regions.Methods:From May to June 2019, the information of AIT cases and healthy individuals in Shandong Province were collected in three types of water-iodine regions: iodine-fortification (IF) region, iodine-adequate (IA) region and iodine-excess (IE) region. A case-control study design was applied to match 176 AIT cases (case group) with age, gender, body mass index, and place of residence in a 1 ∶ 1 ratio to 176 healthy individuals (control group). Fasting urine and whole blood samples were collected to test the contents of urinary iodine, thyroid function indicators [serum free triiodothyronine (FT 3), free thyroxine (FT 4), thyroid stimulating hormone (TSH)], and serum iodine. The DNA methylation levels of the target region of the CDH1 gene and its four CpG sites in whole blood were determined using methylation sequencing technology for target regions (MethylTarget TM). Results:The DNA methylation level of the target region of CDH1 gene in the case group was 0.832 ± 0.044, and that in the control group was 0.828 ± 0.049, there was no statistically significant difference between the two groups ( t = 0.76, P = 0.448). There was no statistically significant difference in DNA methylation levels of the four CpG sites in the target region of CDH1 gene between the case group and the control group ( P > 0.05). There was no statistically significant difference in the DNA methylation level of the CDH1 gene target region between the case group and the control group in IF, IA and IE regions ( P > 0.05). The detection results of DNA methylation levels at CpG sites in the target region of CDH1 gene in different water iodine regions showed that the DNA methylation level at site 83 in case group in IF region was higher than that in the control group ( t = 2.30, P = 0.023). However, there was no statistically significant difference in the DNA methylation levels of the four CpG sites between the case group and the control group in IA and IE regions ( P > 0.05). The DNA methylation level of CDH1 gene target region in AIT patients was not significantly correlated with urinary iodine, serum iodine, and serum FT 3, FT 4, and TSH contents ( P > 0.05), but was significantly negatively correlated with age ( r =-0.19, P = 0.014). Conclusions:The DNA methylation level at CpG site 83 of CDH1 gene in AIT patients in IF region is significantly higher than that in control population, indicating that DNA methylation at this locus may be involved in the occurrence and development of AIT after iodine fortification. The DNA methylation level of CDH1 gene is negatively correlated with age.