Article(id=1324078134349410951, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324078037817504735, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202508081, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1754582400000, receivedDateStr=2025-08-08, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793614427411, onlineDateStr=2026-11-02, pubDate=1770652800000, pubDateStr=2026-02-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793614427411, onlineIssueDateStr=2026-11-02, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793614427411, creator=13701087609, updateTime=1793614427411, updator=13701087609, issue=Issue{id=1324078037817504735, tenantId=1146029695717560320, journalId=1227665162245664772, year='2026', volume='53', issue='3', pageStart='385', pageEnd='576', issueExtLink='null', onlineDate='null', pubDate='1770652800000', pubDateStr='2026-02-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1793614404396, creator='13701087609', updateTime=1793614474144, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1324078330420527919, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324078037817504735, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1324078330420527920, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324078037817504735, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=539, endPage=545, ext={EN=ArticleExt(id=1324078134580097672, articleId=1324078134349410951, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Development and validation of a prediction model for non-alcoholic fatty liver disease in non-obese population using seven machine learning algorithms, columnId=1228016569138213037, journalTitle=Modern Preventive Medicine, columnName=Clinical Medicine and Prevention, runingTitle=null, highlight=null, articleAbstract=
Objective To investigate the predictive value of machine learning methods for non-alcoholic fatty liver disease(NAFLD) in non-obese populations and to analyze the risk factors for NAFLD within this group.
Methods Data on 43 variables,including body composition, medical history, and laboratory test results, were retrospectively collected and screened from 1 517 non-obese subjects at the Physical Examination Center of Xiyuan Hospital, China Academy of Chinese Medical Sciences,between January 2021 and December 2024. The outcome indicator was the diagnosis of NAFLD by abdominal ultrasonography,based on which subjects were stratified into NAFLD and non-NAFLD groups. Predictors were screened using univariate analysis, lasso regression, and multivariable logistic regression analysis. Seven machine learning methods including logistic regression, decision tree, random forest, extreme gradient boosting (XGBoost), light hradient boosting machine(LightGBM),support vector machine(SVM), and artificial neural network(ANN), were employed to develop and validate prediction models for NAFLD in non-obese individuals. The shapley additive explanations (SHAP) method was used to interpret variable contributions.
Results Among the 1 517 included subjects, 215 (14.2%) were diagnosed with NAFLD. Ten independent predictors were identified. The area under the curve (AUC) values for the seven prediction models on the validation set ranged from 0.876 to 0.924. The model based on XGBoost demonstrated the best discriminative ability (AUC=0.924, 95% CI: 0.895-0.949). The SHAP value-based variable importance ranking identified phase angle, the ZJU index, and UHR as the top three significant predictors.
Conclusion The XGBoost model, constructed using routine physical examination data, exhibits high predictive performance for NAFLD incidence in non-obese populations. Its interpretable features facilitate the clinical prioritization of high-risk individuals.
, authors=Lin-jie QIU, Hai-yan REN, Yan REN, Mei-jie LI, Cha-cha ZOU, Zi-jin WU, Jin ZHANG, authorsList=Lin-jie QIU, Hai-yan REN, Yan REN, Mei-jie LI, Cha-cha ZOU, Zi-jin WU, Jin ZHANG, authorCompany=null, correspAuthors=Jin ZHANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1324078137503527571, articleId=1324078134349410951, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=基于七种机器学习算法的非肥胖人群非酒精性脂肪性肝病预测模型的构建与验证, columnId=1228016570119680182, journalTitle=现代预防医学, columnName=临床与预防, runingTitle=null, highlight=null, articleAbstract=
目的 探究机器学习方法对非肥胖人群非酒精性脂肪性肝病（non-alcoholic fatty liver disease，NAFLD）的预测价值，并从中分析非肥胖人群NAFLD的危险因素。
方法 回顾性收集并筛选出2021年1月—2024年12月来自中国中医科学院西苑医院体检中心的1 517名非肥胖研究对象的人体成分、既往史、检验等43个变量信息，结局指标为腹部超声诊断是否为NAFLD，并据此分为NAFLD组和非NAFLD组。通过单因素、LASSO回归和多因素logistic回归分析筛选预测因子，通过logistic回归、决策树模型、随机森林、极端梯度提升、轻量级梯度提升机、支持向量机和人工神经网络七种机器学习方法构建并验证非肥胖NAFLD预测模型，通过采用沙普利加性解释（Shapley additive explanations, SHAP）法解析变量贡献度。
结果 纳入的1 517例研究对象中NAFLD为215例（14.2%）。共筛选出10个独立预测因子，七种预测模型验证集曲线下面积（area under the curve,AUC）值范围为 0.876～0.924，其中基于XGBoost构建的模型区分度最佳（AUC=0.924，95% CI：0.895～0.949）。基于 SHAP 值的变量重要性排序结果显示，相位角、ZJU指数及UHR是前三位重要预测因子。
结论 基于常规体检数据构建的XGBoost模型对非肥胖人群NAFLD的发病具有较高预测效能，其可解释性特征有助于临床优先识别高危个体。
, authors=邱林杰, 任海燕, 任燕, 李美洁, 邹姹姹, 吴梓敬, 张晋, authorsList=邱林杰, 任海燕, 任燕, 李美洁, 邹姹姹, 吴梓敬, 张晋, authorCompany=null, correspAuthors=张晋, authorNote=
邱林杰（1995—），男，博士在读，研究方向：中医治未病、中医药防治代谢性疾病
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本刊刊出的所有文章不代表中华预防医学会和本刊编委会的观点，除非特别声明。, copyrightOwner=中华预防医学会和四川大学华西公共卫生学院, extLink=null, articleAbsUrl=null, sourceXml=o8OBstRDXkaTjVN70n8TKA==, magXml=yTHlWC1+poHWrm0jFa73wg==, pdfUrl=null, pdf=daVGSiwxju+QmBoz45elaw==, pdfFileSize=1077693, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=qlzYkpijwVugo9o4ZqDr0w==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=o/iT4sW1BVf2h9xbi3mBPw==, mapNumber=null, fund=null)}, authors=[Author(id=1324078137860043416, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078134349410951, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=null, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1324078137956512410, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078134349410951, authorId=1324078137860043416, language=EN, stringName=Lin-jie QIU, firstName=Lin-jie, middleName=null, lastName=QIU, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=Department of Physical Examination, Future Disease Center, Xiyuan Hospital, China Academy of Traditional Chinese Medicine,Beijing 100091, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1324078138036204187, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078134349410951, authorId=1324078137860043416, language=CN, stringName=邱林杰, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=中国中医科学院西苑医院治未病中心，北京100091, bio={"content":"
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5(3): 75-80., articleTitle=Low HDL-Cholesterol with normal triglyceride levels is the most common lipid pattern in West Africans and African Americans with metabolic syndrome:implications for cardiovascular disease prevention, refAbstract=null)], funds=[Fund(id=1324078145401402068, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078134349410951, awardId=2018YFC2000600, language=CN, fundingSource=国家重点研发计划资助项目(2018YFC2000600), fundOrder=null, country=null), Fund(id=1324078145489482453, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078134349410951, awardId=2023-SZ-A-51, language=CN, fundingSource=北京中医药“薪火传承3+3工程”(2023-SZ-A-51), fundOrder=null, country=null), Fund(id=1324078145598534358, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078134349410951, awardId=CI2021A03005, language=CN, fundingSource=中国中医科学院科技创新工程(CI2021A03005), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1324078137755185812, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078134349410951, xref=null, ext=[AuthorCompanyExt(id=1324078137763574421, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078134349410951, companyId=1324078137755185812, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Department of Physical Examination, Future Disease Center, Xiyuan Hospital, China Academy of Traditional Chinese Medicine,Beijing 100091, China), AuthorCompanyExt(id=1324078137776157334, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078134349410951, companyId=1324078137755185812, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=中国中医科学院西苑医院治未病中心，北京100091)])], figs=[ArticleFig(id=1324078142205342404, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078134349410951, language=EN, label=Figure 1, caption=
Path diagram of risk factor selection for non-obese NAFLD based on lasso regression analysis, figureFileSmall=jnpc5ed5KK+otiMBC4YkzQ==, figureFileBig=qlzYkpijwVugo9o4ZqDr0w==, tableContent=null), ArticleFig(id=1324078142285034181, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078134349410951, language=CN, label=图1, caption=
基于LASSO回归分析的非肥胖NAFLD发生风险变量选择路径图, figureFileSmall=jnpc5ed5KK+otiMBC4YkzQ==, figureFileBig=qlzYkpijwVugo9o4ZqDr0w==, tableContent=null), ArticleFig(id=1324078142498943686, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078134349410951, language=EN, label=Figure 2, caption=
Cross-validation results of lasso regression analysis screening key characteristics influencing the occurrence of non-obese NAFLD, figureFileSmall=d69mmmSNJjKUYoFvhxZfdQ==, figureFileBig=EpjkcE+QgvWx0g0QXV0ICg==, tableContent=null), ArticleFig(id=1324078142637355719, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078134349410951, language=CN, label=图2, caption=
LASSO回归分析筛选影响非肥胖NAFLD发生的关键特征的交叉验证结果, figureFileSmall=d69mmmSNJjKUYoFvhxZfdQ==, figureFileBig=EpjkcE+QgvWx0g0QXV0ICg==, tableContent=null), ArticleFig(id=1324078142729630408, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078134349410951, language=EN, label=Figure 3, caption=
Test set operating characteristic curve based on machine learning model, figureFileSmall=AcTYBA8O/QYNSvy0jag60Q==, figureFileBig=7R03S8vTrSORPfNb85MBCA==, tableContent=null), ArticleFig(id=1324078142805127881, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078134349410951, language=CN, label=图3, caption=
基于机器学习模型的测试集ROC, figureFileSmall=AcTYBA8O/QYNSvy0jag60Q==, figureFileBig=7R03S8vTrSORPfNb85MBCA==, tableContent=null), ArticleFig(id=1324078142880625354, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078134349410951, language=EN, label=Figure 4, caption=
Feature importance ranking chart, figureFileSmall=kuLSIDY41IXSN4aBaOJntw==, figureFileBig=ABbjursH/BIgCCrBS8xHvQ==, tableContent=null), ArticleFig(id=1324078142981288651, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078134349410951, language=CN, label=图4, caption=
特征重要性排序图, figureFileSmall=kuLSIDY41IXSN4aBaOJntw==, figureFileBig=ABbjursH/BIgCCrBS8xHvQ==, tableContent=null), ArticleFig(id=1324078143052591820, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078134349410951, language=EN, label=Figure 5, caption=
SHAP swarm diagram, figureFileSmall=Mg1JC+sfxGF07XG08vzisQ==, figureFileBig=umucSIQpFs2IZ7RRqJDRdA==, tableContent=null), ArticleFig(id=1324078143140672205, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078134349410951, language=CN, label=图5, caption=
SHAP蜂群图, figureFileSmall=Mg1JC+sfxGF07XG08vzisQ==, figureFileBig=umucSIQpFs2IZ7RRqJDRdA==, tableContent=null), ArticleFig(id=1324078143224558286, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078134349410951, language=EN, label=Table 1, caption=
Comparison of baseline characteristics between the non-obese non-NAFLD group and the non-obese NAFLD group ［n（%），M（P25，P75）］
, figureFileSmall=null, figureFileBig=null, tableContent=
| 变量 | 非肥胖非NAFLD组(n=1 302) | 非肥胖NAFLD组(n=215) | 统计值(χ2/Z值) | P值 |
|---|
| 性别 | | | 9.052 | 0.003 |
| 女性 | 853(65.51) | 118(54.88) | | |
| 男性 | 449(34.49) | 97(45.12) | | |
| 吸烟 | | | 11.814 | 0.002 |
| 否 | 1 285(98.69) | 205(95.35) | | |
| 是 | 17(1.31) | 10(4.65) | | |
| 年龄（岁） | 47.00 (33.00, 62.00) | 49.00 (36.50, 63.00) | -1.620 | 0.106 |
| BMI（kg/m2） | 23.80(21.60, 25.67) | 23.20 (20.60, 25.55) | -2.191 | 0.028 |
| 腰围（cm） | 83.50(77.40, 89.35) | 81.50 (75.65, 88.35) | -2.723 | 0.006 |
| 臀围（cm） | 94.90 (90.82, 98.20) | 94.40(90.00, 97.20) | -2.344 | 0.020 |
| 腰臀比 | 0.88(0.84, 0.92) | 0.87 (0.83, 0.91) | -2.591 | 0.010 |
| 收缩压（mm Hg） | 122.00 (111.00, 134.00) | 129.00(119.50, 140.00) | -5.887 | <0.001 |
| 舒张压（mm Hg） | 70.00 (63.00, 78.00) | 75.00(68.50, 82.00) | -6.072 | <0.001 |
| 中性粒细胞计数（×109/L） | 3.19(2.59, 3.93) | 3.66(3.01, 4.59) | -5.722 | <0.001 |
| 血小板计数（×109/L） | 239.50 (204.00, 279.00) | 254.00 (224.00, 295.00) | -4.220 | <0.001 |
| 单核细胞计数（×109/L） | 0.29(0.24, 0.35) | 0.34(0.28, 0.42) | -6.080 | <0.001 |
| 淋巴细胞计数（×109/L） | 1.84 (1.50, 2.21) | 2.12(1.77, 2.49) | -6.352 | <0.001 |
| ALT（U/L） | 14.10(10.50, 21.30) | 21.70(15.15, 32.20) | -10.543 | <0.001 |
| AST（U/L） | 18.10(15.50, 21.90) | 20.20 (17.05, 25.50) | -5.607 | <0.001 |
| ALT/AST | 0.79 (0.64, 1.02) | 1.05(0.87, 1.40) | -10.991 | <0.001 |
| 总胆红素（μmol/L） | 10.75 (8.40, 14.00) | 11.10 (8.05, 14.20) | -0.093 | 0.931 |
| TC（mmol/L） | 4.74 (4.12, 5.42) | 4.93 (4.20, 5.53) | -2.113 | 0.035 |
| TG（mmol/L） | 1.04 (0.74, 1.46) | 1.37(1.04, 1.91) | -7.934 | <0.001 |
| HDL-C（mmol/L） | 1.34 (1.13, 1.55) | 1.22 (1.02, 1.45) | -5.031 | <0.001 |
| LDL-C（mmol/L） | 2.92 (2.39, 3.56) | 3.16(2.59, 3.76) | -3.090 | 0.002 |
| 肌酐（mmol/L） | 67.00 (59.00, 78.00) | 71.00 (61.00, 83.00) | -2.912 | 0.004 |
| FPG（mmol/L） | 5.30(5.03, 5.73) | 5.57(5.18, 6.19) | -5.584 | <0.001 |
| UA（mmol/L） | 300.00 (253.00, 357.00) | 380.00(338.50, 423.50) | -12.765 | <0.001 |
| NHHR | 2.53(1.89, 3.28) | 2.94(2.38, 3.63) | -5.724 | <0.001 |
| UHR | 0.10(0.07, 0.13) | 0.13(0.09, 0.16) | -8.173 | <0.001 |
| TyG | 6.81(6.43, 7.22) | 7.12 (6.86, 7.54) | -8.572 | <0.001 |
| TyG-BMI | 160.59 (143.52, 176.84) | 166.63(149.58, 181.64) | -2.753 | 0.006 |
| METS-IR | 2.02(1.88, 2.19) | 2.17(2.02, 2.34) | -7.232 | <0.001 |
| ZJU | 34.32 (31.88, 36.48) | 36.27 (33.87, 38.97) | -8.441 | <0.001 |
| 体脂肪（kg） | 19.60(15.90, 23.10) | 18.40 (14.50, 22.40) | -2.676 | 0.007 |
| 肌肉量（kg） | 39.30(35.60, 47.08) | 39.40(34.95, 45.65) | -0.891 | 0.373 |
| 肌脂比 | 2.07(1.72, 2.71) | 2.21(1.77, 2.91) | -1.932 | 0.054 |
| 骨骼肌（kg） | 22.60(20.20, 27.58) | 22.70 (19.75, 26.60) | -1.020 | 0.309 |
| 体脂百分比（%） | 31.25(25.80, 35.38) | 29.80 (24.45, 34.60) | -1.944 | 0.052 |
| 基础代谢率 | 1 272.00 (1 188.00, 1 445.75) | 1 274.00 (1 173.00, 1 412.00) | -0.881 | 0.378 |
| 内脏脂肪面积（cm2） | 89.50(68.90, 112.30) | 89.00(76.00, 107.45) | -0.762 | 0.448 |
| 相位角（°） | 5.05 (4.60, 5.50) | 4.50(4.50, 4.50) | -13.281 | <0.001 |
| 骨骼肌指数 | 6.60 (6.00, 7.40) | 6.60 (6.00, 7.20) | -0.942 | 0.347 |
| 高血压 | | | 26.812 | <0.001 |
| 否 | 973 (74.73) | 124 (57.67) | | |
| 是 | 329 (25.27) | 91 (42.33) | | |
| 糖尿病 | | | 10.802 | 0.002 |
| 否 | 1 157 (88.86) | 174 (80.93) | | |
| 是 | 145 (11.14) | 41 (19.07) | | |
| 胆结石 | | | 75.878 | <0.001 |
| 否 | 1 286 (98.77) | 190 (88.37) | | |
| 是 | 16 (1.23) | 25 (11.63) | | |
), ArticleFig(id=1324078143300055759, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078134349410951, language=CN, label=表1, caption=
非肥胖非NAFLD组和非肥胖NAFLD组基线特征的比较［n（%），M（P25，P75）］
, figureFileSmall=null, figureFileBig=null, tableContent=
| 变量 | 非肥胖非NAFLD组(n=1 302) | 非肥胖NAFLD组(n=215) | 统计值(χ2/Z值) | P值 |
|---|
| 性别 | | | 9.052 | 0.003 |
| 女性 | 853(65.51) | 118(54.88) | | |
| 男性 | 449(34.49) | 97(45.12) | | |
| 吸烟 | | | 11.814 | 0.002 |
| 否 | 1 285(98.69) | 205(95.35) | | |
| 是 | 17(1.31) | 10(4.65) | | |
| 年龄（岁） | 47.00 (33.00, 62.00) | 49.00 (36.50, 63.00) | -1.620 | 0.106 |
| BMI（kg/m2） | 23.80(21.60, 25.67) | 23.20 (20.60, 25.55) | -2.191 | 0.028 |
| 腰围（cm） | 83.50(77.40, 89.35) | 81.50 (75.65, 88.35) | -2.723 | 0.006 |
| 臀围（cm） | 94.90 (90.82, 98.20) | 94.40(90.00, 97.20) | -2.344 | 0.020 |
| 腰臀比 | 0.88(0.84, 0.92) | 0.87 (0.83, 0.91) | -2.591 | 0.010 |
| 收缩压（mm Hg） | 122.00 (111.00, 134.00) | 129.00(119.50, 140.00) | -5.887 | <0.001 |
| 舒张压（mm Hg） | 70.00 (63.00, 78.00) | 75.00(68.50, 82.00) | -6.072 | <0.001 |
| 中性粒细胞计数（×109/L） | 3.19(2.59, 3.93) | 3.66(3.01, 4.59) | -5.722 | <0.001 |
| 血小板计数（×109/L） | 239.50 (204.00, 279.00) | 254.00 (224.00, 295.00) | -4.220 | <0.001 |
| 单核细胞计数（×109/L） | 0.29(0.24, 0.35) | 0.34(0.28, 0.42) | -6.080 | <0.001 |
| 淋巴细胞计数（×109/L） | 1.84 (1.50, 2.21) | 2.12(1.77, 2.49) | -6.352 | <0.001 |
| ALT（U/L） | 14.10(10.50, 21.30) | 21.70(15.15, 32.20) | -10.543 | <0.001 |
| AST（U/L） | 18.10(15.50, 21.90) | 20.20 (17.05, 25.50) | -5.607 | <0.001 |
| ALT/AST | 0.79 (0.64, 1.02) | 1.05(0.87, 1.40) | -10.991 | <0.001 |
| 总胆红素（μmol/L） | 10.75 (8.40, 14.00) | 11.10 (8.05, 14.20) | -0.093 | 0.931 |
| TC（mmol/L） | 4.74 (4.12, 5.42) | 4.93 (4.20, 5.53) | -2.113 | 0.035 |
| TG（mmol/L） | 1.04 (0.74, 1.46) | 1.37(1.04, 1.91) | -7.934 | <0.001 |
| HDL-C（mmol/L） | 1.34 (1.13, 1.55) | 1.22 (1.02, 1.45) | -5.031 | <0.001 |
| LDL-C（mmol/L） | 2.92 (2.39, 3.56) | 3.16(2.59, 3.76) | -3.090 | 0.002 |
| 肌酐（mmol/L） | 67.00 (59.00, 78.00) | 71.00 (61.00, 83.00) | -2.912 | 0.004 |
| FPG（mmol/L） | 5.30(5.03, 5.73) | 5.57(5.18, 6.19) | -5.584 | <0.001 |
| UA（mmol/L） | 300.00 (253.00, 357.00) | 380.00(338.50, 423.50) | -12.765 | <0.001 |
| NHHR | 2.53(1.89, 3.28) | 2.94(2.38, 3.63) | -5.724 | <0.001 |
| UHR | 0.10(0.07, 0.13) | 0.13(0.09, 0.16) | -8.173 | <0.001 |
| TyG | 6.81(6.43, 7.22) | 7.12 (6.86, 7.54) | -8.572 | <0.001 |
| TyG-BMI | 160.59 (143.52, 176.84) | 166.63(149.58, 181.64) | -2.753 | 0.006 |
| METS-IR | 2.02(1.88, 2.19) | 2.17(2.02, 2.34) | -7.232 | <0.001 |
| ZJU | 34.32 (31.88, 36.48) | 36.27 (33.87, 38.97) | -8.441 | <0.001 |
| 体脂肪（kg） | 19.60(15.90, 23.10) | 18.40 (14.50, 22.40) | -2.676 | 0.007 |
| 肌肉量（kg） | 39.30(35.60, 47.08) | 39.40(34.95, 45.65) | -0.891 | 0.373 |
| 肌脂比 | 2.07(1.72, 2.71) | 2.21(1.77, 2.91) | -1.932 | 0.054 |
| 骨骼肌（kg） | 22.60(20.20, 27.58) | 22.70 (19.75, 26.60) | -1.020 | 0.309 |
| 体脂百分比（%） | 31.25(25.80, 35.38) | 29.80 (24.45, 34.60) | -1.944 | 0.052 |
| 基础代谢率 | 1 272.00 (1 188.00, 1 445.75) | 1 274.00 (1 173.00, 1 412.00) | -0.881 | 0.378 |
| 内脏脂肪面积（cm2） | 89.50(68.90, 112.30) | 89.00(76.00, 107.45) | -0.762 | 0.448 |
| 相位角（°） | 5.05 (4.60, 5.50) | 4.50(4.50, 4.50) | -13.281 | <0.001 |
| 骨骼肌指数 | 6.60 (6.00, 7.40) | 6.60 (6.00, 7.20) | -0.942 | 0.347 |
| 高血压 | | | 26.812 | <0.001 |
| 否 | 973 (74.73) | 124 (57.67) | | |
| 是 | 329 (25.27) | 91 (42.33) | | |
| 糖尿病 | | | 10.802 | 0.002 |
| 否 | 1 157 (88.86) | 174 (80.93) | | |
| 是 | 145 (11.14) | 41 (19.07) | | |
| 胆结石 | | | 75.878 | <0.001 |
| 否 | 1 286 (98.77) | 190 (88.37) | | |
| 是 | 16 (1.23) | 25 (11.63) | | |
), ArticleFig(id=1324078143392330448, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078134349410951, language=EN, label=Table 2, caption=
Multivariable logistic regression analysis of risk factors for NAFLD in non-obese patients
, figureFileSmall=null, figureFileBig=null, tableContent=
| 变量 | 偏回归系数 | 标准误 | Wald χ2值 | P值 |
|---|
| 血小板计数(×109/L) | 0.006 | 0.003 | 2.418 | 0.016 |
| 相位角(。) | -2.277 | 0.246 | -9.266 | <0.001 |
| 体脂肪(kg) | -0.192 | 0.045 | -4.294 | <0.001 |
| ALT(U/L) | -0.046 | 0.023 | -1.985 | 0.047 |
| AST(U/L) | 0.0789 | 0.030 | 2.613 | 0.009 |
| HDL-C(mmol/L) | -3.398 | 1.042 | -3.262 | 0.001 |
| TG(mmol/L) | -0.339 | 0.130 | -2.608 | 0.009 |
| UHR | -38.183 | 10.100 | -3.780 | <0.001 |
| ZJU | 0.598 | 0.081 | 7.363 | <0.001 |
| 胆结石 | 2.491 | 0.587 | 4.243 | <0.001 |
), ArticleFig(id=1324078143497188049, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078134349410951, language=CN, label=表2, caption=
非肥胖患者NAFLD风险影响因素的多因素logistic回归分析
, figureFileSmall=null, figureFileBig=null, tableContent=
| 变量 | 偏回归系数 | 标准误 | Wald χ2值 | P值 |
|---|
| 血小板计数(×109/L) | 0.006 | 0.003 | 2.418 | 0.016 |
| 相位角(。) | -2.277 | 0.246 | -9.266 | <0.001 |
| 体脂肪(kg) | -0.192 | 0.045 | -4.294 | <0.001 |
| ALT(U/L) | -0.046 | 0.023 | -1.985 | 0.047 |
| AST(U/L) | 0.0789 | 0.030 | 2.613 | 0.009 |
| HDL-C(mmol/L) | -3.398 | 1.042 | -3.262 | 0.001 |
| TG(mmol/L) | -0.339 | 0.130 | -2.608 | 0.009 |
| UHR | -38.183 | 10.100 | -3.780 | <0.001 |
| ZJU | 0.598 | 0.081 | 7.363 | <0.001 |
| 胆结石 | 2.491 | 0.587 | 4.243 | <0.001 |
), ArticleFig(id=1324078143576879826, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078134349410951, language=EN, label=Table 3, caption=
Screening results for seven machine model test sets
, figureFileSmall=null, figureFileBig=null, tableContent=
| 机器学习方法 | AUC (95%CI) | 灵敏度（%） | 特异度（%） | 约登指数（%） |
|---|
| logistic | 91.01(87.87~93.97) | 51.56 | 94.10 | 45.67 |
| DT | 87.6(84.51~90.43) | 96.88 | 71.03 | 67.90 |
| 随机森林 | 91.47(89.90~94.36) | 90.63 | 81.79 | 72.42 |
| XGBoost | 92.36(89.47~94.91) | 92.19 | 82.56 | 74.75 |
| LightGBM | 91.17(88.10~94.10) | 90.63 | 77.95 | 68.57 |
| SVM | 87.75(83.48~91.87) | 76.56 | 81.54 | 58.10 |
| ANN | 88.89(84.51~92.85) | 78.13 | 81.79 | 59.92 |
), ArticleFig(id=1324078145279767251, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078134349410951, language=CN, label=表3, caption=
七种机器模型测试集筛检结果
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| 机器学习方法 | AUC (95%CI) | 灵敏度（%） | 特异度（%） | 约登指数（%） |
|---|
| logistic | 91.01(87.87~93.97) | 51.56 | 94.10 | 45.67 |
| DT | 87.6(84.51~90.43) | 96.88 | 71.03 | 67.90 |
| 随机森林 | 91.47(89.90~94.36) | 90.63 | 81.79 | 72.42 |
| XGBoost | 92.36(89.47~94.91) | 92.19 | 82.56 | 74.75 |
| LightGBM | 91.17(88.10~94.10) | 90.63 | 77.95 | 68.57 |
| SVM | 87.75(83.48~91.87) | 76.56 | 81.54 | 58.10 |
| ANN | 88.89(84.51~92.85) | 78.13 | 81.79 | 59.92 |
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