BACKGROUND:Studies have shown that rheumatoid arthritis and osteoporosis are positively correlated,but the causal relationship and related mechanisms have not yet been confirmed.With the cross-fertilization of computer science and life sciences,Mendelian randomization and bioinformatics analyses based on genome-wide association study(GWAS)and transcriptome sequencing data can assess the causal relationship between two diseases,explore the related mechanisms,and mine the therapeutic targets,which will be beneficial to the precision treatment of rheumatoid arthritis combined with osteoporosis.OBJECTIVE:To explore the causal relationship between rheumatoid arthritis and osteoporosis using two-sample Mendelian randomization and to mine potential co-morbid targets and potential targeted drugs through summary-data-based Mendelian randomization and bioinformatics analyses,aiming to provide theoretical basis for mechanism exploration and precision treatment in the field of rheumatoid arthritis combined with osteoporosis.METHODS:(1)Firstly,GWAS data of rheumatoid arthritis,osteoporosis,and cis-expression quantitative trait locus(cis-eQTL)in Asian and European populations were downloaded from the GWAS Catalog,IEU Open GWAS,FinnGen,and eQTLGen databases,and were used for two-sample Mendelian randomization analysis and summary-data-based Mendelian randomization analysis.(2)Transcriptome sequencing data of rheumatoid arthritis(GSE93272 and GSE15573)were downloaded from the GEO database for bioinformatics analysis.(3)Subsequently,forward and inverse Mendelian randomization analyses between rheumatoid arthritis and osteoporosis were performed,and inverse variance weighted was used as the main metric for the analyses,and the results were corroborated with MR Egger,simple mode,weighted median and weighted mode.(4)Then,the genes closely related to rheumatoid arthritis and osteoporosis were identified based on the summary-data-based Mendelian randomization analysis,and the co-disease targets of rheumatoid arthritis and osteoporosis were mined based on cross-analysis.Meanwhile,the biological functions of the co-morbid targets were verified based on bioinformatics analysis and cellular experiments.(5)In addition,a rheumatoid arthritis risk prediction nomogram was constructed based on DYRK2,and its prediction performance was verified by receiver operating characteristic curve,correction curve and decision curve.Finally,the target potential drugs were mined based on Enrichr database and molecular docking was performed.RESULTS AND CONCLUSION:(1)Forward Mendelian randomization analysis of rheumatoid arthritis and osteoporosis showed statistically significant results except for GCST90044540 and GCST90086118,and all other results indicated a significant causal relationship and positive correlation between rheumatoid arthritis and osteoporosis.(2)Inverse Mendelian randomization analysis suggested that no significant causal relationship was seen between osteoporosis and rheumatoid arthritis.(3)Summary-data-based Mendelian randomization analysis identified a total of 412 and 344 genes positively associated with rheumatoid arthritis and osteoporosis,and 421 and 347 genes negatively associated.Based on the cross-analysis,26 co-morbid genes were subsequently obtained.Among them,DYRK2 was a potential therapeutic target,and subsequent bioinformatics analysis and cellular experiments confirmed its important role in the progression of rheumatoid arthritis and osteoporosis.(4)Furthermore,the constructed nomogram has excellent predictive performance.Finally,four potential DYRK2-targeting drugs(undecanoic acid,metyrapone,JNJ-38877605,and ACA)were discovered and molecular docking also demonstrated reliable targeting ability.(5)In conclusion,based on GWAS data from Asian and European populations,we successfully demonstrated that rheumatoid arthritis and osteoporosis are causally related at the genetic level,DYRK2 is a potential therapeutic target,and four small molecules are potential target drugs.

BACKGROUND:Maxillofacial bone three-dimensional(3D)printing technology has been widely used in clinical diagnosis and treatment,but the data source before performing maxillofacial bone 3D printing mainly comes from the CT scanning data.The lens,thyroid and other parts of the human body are extremely sensitive to X-rays;therefore,it is particularly important to effectively reduce the dose of CT radiation when acquiring the data source.OBJECTIVE:To explore the feasibility of low-dose CT technology in optimizing radiation dose for maxillofacial bone 3D printing.METHODS:The medical records of 65 patients who underwent maxillofacial bone 3D printing in the Department of Stomatology at the General Hospital of Northern Theater Command from March 2021 to December 2023 were retrospectively collected and categorized into a conventional CT-dose 3D printing group(conventional CT-dose,120 kVp,automated tube current modulation,n=32)and a low-CT-dose 3D printing group(low-CT-dose group,80 kVp,automated tube current modulation,n=33).The effective dose of radiation was calculated and compared between the two groups.A Likert scale was used to evaluate the quality of 3D printing in the two groups,and the measurement bias and consistency between evaluators were measured using the Bland-Altman method.RESULTS AND CONCLUSION:(1)There was no significant difference in the general demographic characteristics(age,height,weight,body mass,sex,and body mass index)between the two groups(all P＞0.05).(2)The effective dose value of the low CT-dose 3D printing group was(0.3±0.1)mSv,which was about 62.5％lower than that in the conventional CT-dose 3D printing group[(0.8±0.1)mSv].(3)There was no significant difference in the subjective scoring of 3D printing quality between the two groups(all P＞0.05).The subjective consistency among evaluators was good,with Kappa values of 0.85,0.80,and 0.76.The scatter points in the Bland-Altman for both protocols were uniformly distributed within the standard deviation line,indicating good consistency between the two groups.To conclude,low-dose CT technology can be effectively applied in maxillofacial bone 3D printing,reducing radiation dose without affecting the quality of 3D printing.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

BACKGROUND:Studies have shown that rheumatoid arthritis and osteoporosis are positively correlated,but the causal relationship and related mechanisms have not yet been confirmed.With the cross-fertilization of computer science and life sciences,Mendelian randomization and bioinformatics analyses based on genome-wide association study(GWAS)and transcriptome sequencing data can assess the causal relationship between two diseases,explore the related mechanisms,and mine the therapeutic targets,which will be beneficial to the precision treatment of rheumatoid arthritis combined with osteoporosis.OBJECTIVE:To explore the causal relationship between rheumatoid arthritis and osteoporosis using two-sample Mendelian randomization and to mine potential co-morbid targets and potential targeted drugs through summary-data-based Mendelian randomization and bioinformatics analyses,aiming to provide theoretical basis for mechanism exploration and precision treatment in the field of rheumatoid arthritis combined with osteoporosis.METHODS:(1)Firstly,GWAS data of rheumatoid arthritis,osteoporosis,and cis-expression quantitative trait locus(cis-eQTL)in Asian and European populations were downloaded from the GWAS Catalog,IEU Open GWAS,FinnGen,and eQTLGen databases,and were used for two-sample Mendelian randomization analysis and summary-data-based Mendelian randomization analysis.(2)Transcriptome sequencing data of rheumatoid arthritis(GSE93272 and GSE15573)were downloaded from the GEO database for bioinformatics analysis.(3)Subsequently,forward and inverse Mendelian randomization analyses between rheumatoid arthritis and osteoporosis were performed,and inverse variance weighted was used as the main metric for the analyses,and the results were corroborated with MR Egger,simple mode,weighted median and weighted mode.(4)Then,the genes closely related to rheumatoid arthritis and osteoporosis were identified based on the summary-data-based Mendelian randomization analysis,and the co-disease targets of rheumatoid arthritis and osteoporosis were mined based on cross-analysis.Meanwhile,the biological functions of the co-morbid targets were verified based on bioinformatics analysis and cellular experiments.(5)In addition,a rheumatoid arthritis risk prediction nomogram was constructed based on DYRK2,and its prediction performance was verified by receiver operating characteristic curve,correction curve and decision curve.Finally,the target potential drugs were mined based on Enrichr database and molecular docking was performed.RESULTS AND CONCLUSION:(1)Forward Mendelian randomization analysis of rheumatoid arthritis and osteoporosis showed statistically significant results except for GCST90044540 and GCST90086118,and all other results indicated a significant causal relationship and positive correlation between rheumatoid arthritis and osteoporosis.(2)Inverse Mendelian randomization analysis suggested that no significant causal relationship was seen between osteoporosis and rheumatoid arthritis.(3)Summary-data-based Mendelian randomization analysis identified a total of 412 and 344 genes positively associated with rheumatoid arthritis and osteoporosis,and 421 and 347 genes negatively associated.Based on the cross-analysis,26 co-morbid genes were subsequently obtained.Among them,DYRK2 was a potential therapeutic target,and subsequent bioinformatics analysis and cellular experiments confirmed its important role in the progression of rheumatoid arthritis and osteoporosis.(4)Furthermore,the constructed nomogram has excellent predictive performance.Finally,four potential DYRK2-targeting drugs(undecanoic acid,metyrapone,JNJ-38877605,and ACA)were discovered and molecular docking also demonstrated reliable targeting ability.(5)In conclusion,based on GWAS data from Asian and European populations,we successfully demonstrated that rheumatoid arthritis and osteoporosis are causally related at the genetic level,DYRK2 is a potential therapeutic target,and four small molecules are potential target drugs.

BACKGROUND:Maxillofacial bone three-dimensional(3D)printing technology has been widely used in clinical diagnosis and treatment,but the data source before performing maxillofacial bone 3D printing mainly comes from the CT scanning data.The lens,thyroid and other parts of the human body are extremely sensitive to X-rays;therefore,it is particularly important to effectively reduce the dose of CT radiation when acquiring the data source.OBJECTIVE:To explore the feasibility of low-dose CT technology in optimizing radiation dose for maxillofacial bone 3D printing.METHODS:The medical records of 65 patients who underwent maxillofacial bone 3D printing in the Department of Stomatology at the General Hospital of Northern Theater Command from March 2021 to December 2023 were retrospectively collected and categorized into a conventional CT-dose 3D printing group(conventional CT-dose,120 kVp,automated tube current modulation,n=32)and a low-CT-dose 3D printing group(low-CT-dose group,80 kVp,automated tube current modulation,n=33).The effective dose of radiation was calculated and compared between the two groups.A Likert scale was used to evaluate the quality of 3D printing in the two groups,and the measurement bias and consistency between evaluators were measured using the Bland-Altman method.RESULTS AND CONCLUSION:(1)There was no significant difference in the general demographic characteristics(age,height,weight,body mass,sex,and body mass index)between the two groups(all P＞0.05).(2)The effective dose value of the low CT-dose 3D printing group was(0.3±0.1)mSv,which was about 62.5％lower than that in the conventional CT-dose 3D printing group[(0.8±0.1)mSv].(3)There was no significant difference in the subjective scoring of 3D printing quality between the two groups(all P＞0.05).The subjective consistency among evaluators was good,with Kappa values of 0.85,0.80,and 0.76.The scatter points in the Bland-Altman for both protocols were uniformly distributed within the standard deviation line,indicating good consistency between the two groups.To conclude,low-dose CT technology can be effectively applied in maxillofacial bone 3D printing,reducing radiation dose without affecting the quality of 3D printing.

Objective To explore the construction of heart preservation model of empty beating donor based on extracorporeal membrane oxygenation (ECMO). Methods From January 2022 to August 2023, 20 Guangxi Bama miniature pigs weighing 25-30 kg were selected, half male and half female. Under general anesthesia and heparinization, a midline thoracotomy was performed. The pericardium was cut after freeing the anterior and posterior vena cavae, and a perfusion needle was inserted near the brachiocephalic artery in the ascending aorta, connected to a blood collection bag to collect 500-600 mL of blood. The anterior and posterior vena cavae were ligated, the aorta was blocked and perfused with HTK solution to stop the heart beating. The superior and inferior vena cavae were cut off, the right pulmonary vein was decompressed, the aorta and left and right pulmonary arteries and veins were cut off, and the whole heart was removed. An ECMO device was used to continuously perfuse a cardioprotective solution mainly composed of oxygenated warm blood, maintaining the isolated pig heart beating for 8 hours, monitoring (once/hour) ECMO perfusion parameters, blood gas indicators, perfusate electrolytes, inflammatory factors, myocardial enzymes, myoglobin, and troponin levels. Myocardial tissue was taken for hematoxylin-eosin (HE) staining to observe myocardial cell damage and evaluate the quality of heart preservation. Results Among the 20 isolated beating pig hearts, 17 successfully resumed beating, 3 experienced ventricular fibrillation, resuscitated after intracardiac electrical defibrillation, and all 20 pig hearts successfully beat for 8 hours. There was no statistical difference in ECMO perfusion parameters, blood gas indicators, perfusate electrolytes, and inflammatory factors at each time point (P>0.05). There were statistical increases in myocardial enzymes, myoglobin, and troponin levels (P<0.05). HE staining results suggested that there was no severe myocardial damage. Conclusion ECMO technology can be used for pig heart preservation with good results, and this study provides experimental evidence for improving heart preservation research in clinical heart transplantation.

Objective: To systematically evaluate the performance of machine learning (ML) models in predicting non suicidal self injury (NSSI) behavior among adolescents, providing an evidence based foundation for the development of clinically applicable risk assessment tools. Methods: A comprehensive search was conducted in PubMed, Embase, Web of Science, CNKI, and Wanfang databases for relevant studies from their inception to July 21, 2025. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to evaluate the methodological quality of the included studies. Stata 18 software was used to calculate the area under the receiver operating characteristic curve (AUC) of the models, and publication bias assessment, sensitivity analysis, and Egger s test were performed. Results: The total of 12 studies (42 prediction models) involving 58 070 adolescents were included. There were 15 machine learning algorithms in total, among which Random Forest, Logistic Regression, XGBoost, and Support Vector Machines were the most frequently utilized. The most common predictors were gender (female), family function, depression, emotion regulation, and age. All 12 included studies exhibited a high risk of bias. The pooled AUC was 0.80 (95% CI =0.78-0.82), although heterogeneity was substantial ( I 2=95.8%, P <0.01). Sensitivity analysis confirmed the robustness of these findings (no overlap in 95% CI ), while Egger s test indicated the presence of publication bias ( P <0.05). Conclusions Machine learning demonstrates potential in the risk prediction of adolescent NSSI, but existing models have a high risk of bias. Future research should focus on improving methodological quality and optimizing model reliability through rigorous external validation.

ObjectiveTo evaluate the comprehensive intervention effects of Huangqin Qingre Chubi Capsule （黄芩清热除痹胶囊， HQC） on self-perception of patients （SPP） and immune inflammation in patients with rheumatoid arthritis （RA）， and to explore its potential mechanisms. MethodsClinical data of 452 RA patients were retrospectively collected. Patients were divided into a control group （274 cases）， treated with conventional western medicine， and an observation group （178 cases）， treated with HQC for at least 2 weeks in addition to conventional western medicine. The treatment duration was 2 weeks for both groups. Propensity score matching （PSM） was performed at a ratio of 1∶1 to match patients between groups. SPP including the Chinese version of the short form-36 health survey （SF-36）， self-rating anxiety scale （SAS）， self-rating depression scale （SDS）， visual analog scale （VAS）， and Chinese patient-reported index for rheumatoid arthritis （CPRI-RA）， as well as immune inflammatory indicators， including erythrocyte sedimentation rate （ESR）， high-sensitivity C-reactive protein （hs-CRP）， rheumatoid factor （RF）， anti-cyclic citrullinated peptide antibody （anti-CCP）， interleukin-6 （IL-6）， immunoglobulin A （IgA）， immunoglobulin G （IgG）， immunoglobulin M （IgM）， complement C3， and complement C4， were collected before and after treatment. Spearman correlation analysis was used to assess the relationships between SPP and immune inflammatory indicators. Logistic regression， association rule analysis， and mediation analysis were performed to evaluate the effects and potential pathways of HQC on SPP and immune inflammatory indicators. Network pharmacology was applied to identify the active components and core targets of HQC in the treatment of RA， followed by molecular docking verification. In cell experiments， cells were divided into normal group， model group， 20% medicated serum group， and 80 nmol/L control group. Human synovial fibroblasts （FLS） were cultured with complete medium in the normal group， while human rheumatoid arthritis fibroblast-like synoviocytes （RA-FLS） were cultured in the model group. In the 20% medicated serum group， RA-FLS were cultured with medium containing 20% HQC-medicated serum， and in the 80 nmol/L control group， RA-FLS were cultured with complete medium containing 80 nmol/L methotrexate suspension. After 48 h of culture， cell viability was detected by cell counting kit-8 （CCK-8） assay. Levels of interleukin-1β （IL-1β）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， and interleukin-10 （IL-10） in the cell supernatant were measured by enzyme-linked immunosorbent assay （ELISA）. Protein levels of matrix metalloproteinase 9 （MMP9）， transcription factor AP-1 subunit （JUN）， vascular endothelial growth factor A （VEGFA）， and C-X-C motif chemokine ligand 8 （CXCL8） were detected by Western Blot， and cell migration ability was evaluated using Transwell assay. ResultsAfter PSM， 178 cases were included in each group. After treatment， SF-36 scores increased， while scores of SAS， SDS， VAS and CPRI-RA， levels of ESR， hs-CRP， IL-6， complement C3， and complement C4 levels decreased in both groups； IgG and IgM levels were also reduced in the observation group （P＜0.05）. Physical functioning （correlation coefficient -0.19， P＜0.05） and social functioning （correlation coefficient -0.18， P＜0.05） of SF-36 were negatively correlated with hs-CRP， while VAS score was positively correlated with hs-CRP （correlation coefficient 0.19， P＜0.05）. HQC showed high associations with improvements in multiple indicators of SPP and immune inflammatory， and acted as a protective factor for the improvement of several SPP； hs-CRP and ESR played partial mediating roles in the improvement of SPP induced by HQC （P＜0.05）. Network pharmacology analysis identified baicalein， quercetin， α1-sitosterol， β-sitosterol， stigmasterol， baicalin， and crocetin as the core active components， and JUN， IL-6， VEGFA， MMP9， IL-1β， and CXCL8 as the core targets. Molecular docking results showed strong binding affinities of quercetin with VEGFA， JUN， MMP9， IL-6， and IL-1β， of baicalin with VEGFA and MMP9， and of wogonin with CXCL8. Cell experiments demonstrated that HQC and methotrexate inhibited RA-FLS viability and migration， reduced levels of IL-1β， IL-6， and IL-8， decreased protein levels of MMP9， JUN， VEGFA， and CXCL8， and increased IL-10 levels （P＜0.05）. ConclusionHQC can improve SPP in RA by regulating immune inflammatory responses. Its mechanism may be related to multi-pathway and multi-target inhibition of synovial cell inflammation and migration.

BackgroundPersistent cognitive impairment is prevalent among patients with stable schizophrenia. While serum total bile acid （TBA） level in acute-phase patients are known to be associated with cognitive dysfunction， the relationship between serum TBA and multi-dimensional cognitive functions in stable phase patients remains unclear. ObjectiveTo investigate the correlation between serum TBA level and cognitive function in patients with stable schizophrenia， and to evaluate its predictive value for cognitive impairment， thereby providing a serological biomarker for the timely identification and objective assessment of cognitive dysfunction. MethodsA cross-sectional study was conducted on 137 inpatients with stable schizophrenia at The Fourth People's Hospital of Yancheng from March to December 2024. All participants met the diagnostic criteria of the Diagnostic and Statistical Manual of Mental Disorders， fifth edition （DSM-5）. Cognitive function was evaluated using the Chinese Brief Cognitive Test （C-BCT）， patients were categorized into four groups： normal cognition （n=28）， mild impairment （n=28）， moderate impairment （n=47）， and severe impairment （n=34）. Fasting venous blood samples were collected， and serum TBA level was quantified using an enzymatic cycle assay. Spearman correlation analysis was ultilized to determine the relationship between serum TBA level， overall cognitive function， and specific cognitive domains. Binary Logistic regression model was used （adjusting for covariates such as age， gender， and disease duration） to analyze the impact of serum TBA level on overall and individual cognitive functions. The predictive value of serum TBA level for overall cognitive impairment was evaluated using receiver operating characteristic （ROC） curve. ResultsSerum TBA levels differed significantly among the four groups （H=18.677， P<0.01）. Specifically， serum TBA levels in both the moderate and severe cognitive impairment groups were significantly higher than those in the normal cognitive group （adjusted P<0.01）. Serum TBA level was positively correlated with the severity grading of overall cognitive impairment （r_s=0.354， P<0.05）， and negatively correlated with T-scores on the trail making test （r_s=-0.328， P<0.05）， continuous performance test （r_s=-0.247， P<0.05）， digit span （r_s=-0.265， P<0.05）， and symbol coding （r_s=-0.221， P<0.05）. Binary Logistic regression analysis identified serum TBA level as an independent risk factor for overall cognitive impairment （OR=1.322， 95% CI： 1.021 - 1.713， P=0.034）， with a particularly robust predictive ability for impaired information processing speed （OR=1.325， 95% CI： 1.057 - 1.661， P=0.015）. The area under ROC curve （AUC） for serum TBA level in predicting overall cognitive impairment was 0.738， with a sensitivity of 60.61% and a specificity of 78.64%. ConclusionIn patients with stable schizophrenia， elevated serum TBA levels are associated with worse overall cognitive function， as well as deficits in information processing speed， attention， working memory， and executive function. Serum TBA serves as an independent risk factor and exhibits moderate predictive value for overall cognitive impairmen，particularly in the domain of information processing speed. ［Funded by Yancheng Municipal Health Commission Medical Research Project （number， YK2024141）］

In recent years， the incidence of depression among adolescents has increased significantly， becoming a serious global public health problem. There is a close relationship between adolescent depression and lipid metabolism abnormalities. However， the mechanisms underlying this association have not been fully elucidated. This article provides a review of the mechanisms and interventions linking adolescent depression and lipid metabolism abnormalities， aiming to offer new ideas for interventions in adolescent depression. Using computerized searches of the China National Knowledge Infrastructure （CNKI）， Wanfang Data， PubMed， and Web of Science， studies on the mechanisms and interventions related to adolescent depression and lipid metabolism were collected， with the search period extending up to October 10， 2025. Two researchers independently conducted literature screening and extracted basic information from the included studies. A total of 12 original studies were included. The results indicate that adolescent depression is associated with certain lipid metabolites. Neurobiological factors， immune inflammation， oxidative stress， and gut microbiota imbalance play important roles in this association. In addition， lipid metabolites such as omega-3 polyunsaturated fatty acids （ω-3 PUFAs） have a significant ameliorating effect on adolescent depression. ［Funded by Shenzhen Fund for Guangdong Provincial High-level Clinical Key Specialties （number， SZGSP013）； Shenzhen Science and Technology Program （number， 20210617155253001）］