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Article(id=1322210979845358311, tenantId=1146029695717560320, journalId=1319373162502258839, issueId=1322210968248107603, articleNumber=null, orderNo=null, doi=10.16016/j.2097-0927.202412024, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1733414400000, receivedDateStr=2024-12-06, revisedDate=1739721600000, revisedDateStr=2025-02-17, acceptedDate=null, acceptedDateStr=null, onlineDate=1793169263087, onlineDateStr=2026-10-28, pubDate=1747238400000, pubDateStr=2025-05-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793169263087, onlineIssueDateStr=2026-10-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793169263087, creator=13701087609, updateTime=1793169263087, updator=13701087609, issue=Issue{id=1322210968248107603, tenantId=1146029695717560320, journalId=1319373162502258839, year='2025', volume='47', issue='9', pageStart='893', pageEnd='1028', issueExtLink='null', onlineDate='null', pubDate='1747238400000', pubDateStr='2025-05-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793169260321, creator='13701087609', updateTime=1793325292291, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322865413818536367, tenantId=1146029695717560320, journalId=1319373162502258839, issueId=1322210968248107603, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322865413818536368, tenantId=1146029695717560320, journalId=1319373162502258839, issueId=1322210968248107603, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=969, endPage=979, ext={EN=ArticleExt(id=1322210980138959592, articleId=1322210979845358311, tenantId=1146029695717560320, journalId=1319373162502258839, language=EN, title=Construction and validation of a risk prediction model for lean metabolic dysfunction-associated steatotic liver disease, columnId=1320435174995772212, journalTitle=Journal of Army Medical University, columnName=Clinical Medicine, runingTitle=null, highlight=null, articleAbstract=
Objective‍

To investigate the risk factors for the occurrence of metabolic dysfunction-associated steatotic liver disease(MASLD) in lean individuals and construct a risk prediction model.

Methods‍

Based on the National Health and Nutrition Examination Survey(NHANES) database in the United States(from January 2017 to March 2020), 1 123 adult individuals were included in this study. Then the participants were randomly divided into a training set(n=561) and a validation set(n=562) through simple random sampling. Data on their demographics, anthropometrics, lifestyle, underlying diseases, and laboratory test results were collected. LASSO regression analysis was used to screen potential variables in the training set, and multivariate logistic regression was employed to identify independent risk factors for lean MASLD. Based on these risk factors, a prediction model for lean MASLD was constructed(LMPM). To evaluate the clinical value of the LMPM, it was compared with two commonly used prediction models for non-alcoholic fatty liver disease, the fatty liver index(FLI) and the hepatic steatosis index(HSI). The performance of the model was evaluated and internally validated using the area under the receiver operating characteristic curve(AUC), net reclassification index(NRI), integrated discrimination improvement(IDI), calibration curve, decision curve, and clinical impact curve.

Results‍

Age, waist circumference, and triglycerides(TG) were identified as independent risk factors for the development of MASLD in lean individuals. The LMPM, constructed based on these indicators, demonstrated good discriminative ability in both the training and validation sets, with AUC values of 0.86(95%CI: 0.82~0.89) and 0.81(95%CI: 0.77~0.85), respectively, which were significantly better than those of FLI [training set: AUC=0.83(95%CI: 0.79~0.87); validation set: AUC=0.74(95%CI: 0.70~0.79)] and HSI [training set: AUC=0.71(95%CI: 0.66~0.76); validation set: AUC=0.71(95%CI: 0.65~0.76)]. Compared with FLI and HSI, the LMPM showed improvements in NRI and IDI in both the training and validation sets. The calibration curve demonstrated its high accuracy, and both the decision curve analysis and the clinical impact curve analysis indicated that the LMPM provided greater clinical benefits.

Conclusion‍

Age, waist circumference, and TG are independent risk factors for lean MASLD. Based on these factors, a prediction model, named LMPM, is developed to assess the risk of MASLD in lean individuals, which exhibits good predictive performance and has certain guiding significance for the timely identification of high-risk populations.

, authors=Jiayi QIN, Xueqian ZHOU, Xiangyong MENG, Wensheng CHEN, authorsList=Jiayi QIN, Xueqian ZHOU, Xiangyong MENG, Wensheng CHEN, authorCompany=null, correspAuthors=Wensheng CHEN, authorNote=null, correspAuthorsNote=null, copyrightStatement=© 2025 Journal of Army Medical University, copyrightOwner=Editorial Office of Journal of Army Medical University, 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=1322210981422416629, articleId=1322210979845358311, tenantId=1146029695717560320, journalId=1319373162502258839, language=CN, title=瘦型代谢功能障碍相关脂肪性肝病风险预测模型的构建与验证, columnId=1322185541475488183, journalTitle=陆军军医大学学报, columnName=临床医学, runingTitle=null, highlight=null, articleAbstract=
目的

探讨瘦型人群发生代谢功能障碍相关脂肪性肝病（metabolic dysfunction-associated steatotic liver disease，MASLD）的危险因素，并构建风险预测模型。

方法

本研究基于美国国家健康和营养检查调查数据库（2017年1月至2020年3月），纳入1 123名成年个体。通过简单随机抽样将其分为训练集（n=561）和验证集（n=562），并收集有关人口统计学、人体测量学、生活方式、基础疾病和实验室检查结果的数据。在训练集中运用LASSO回归筛选潜在变量，再通过多因素Logistic回归确定瘦型MASLD的独立危险因素。基于这些危险因素，构建瘦型MASLD预测模型（prediction model for lean metabolic dysfunction-associated steatotic liver disease，LMPM）。为评估 LMPM的临床价值，将其与脂肪肝指数（fatty liver index，FLI）和肝脂肪变性指数（hepatic steatosis index，HSI）这两种常用的非酒精性脂肪性肝病预测模型进行比较。通过受试者工作特征曲线下面积（area under the receiver operating characteristic curve，AUC）、净重新分类指数（net reclassification index，NRI）、综合判别改善指数（integrated discrimination improvement，IDI）、校准曲线、决策曲线和临床影响曲线对模型的性能进行评价和内部验证。

结果

本研究确定年龄、腰围和甘油三酯为瘦型人群发生MASLD的独立危险因素。基于上诉指标建立的LMPM在训练集和验证集中表现出良好的区分能力，AUC值分别为0.86（95%CI：0.82~0.89）和0.81（95%CI：0.77~0.85），明显优于FLI［训练集AUC=0.83（95%CI：0.79~0.87）；验证集AUC=0.74（95%CI：0.70~0.79）］和HSI［训练集AUC=0.71（95%CI：0.66~0.76）；验证集AUC=0.71（95%CI：0.65~0.76）］。相较于FLI和HSI，LMPM在训练集和验证集中NRI和IDI均得到改善。校准曲线显示出LMPM的高准确性，决策曲线和临床影响曲线均表明LMPM提供了更大的临床益处。

结论

年龄、腰围和甘油三酯是瘦型MASLD的独立危险因素，据此构建的用于评估瘦型人群发生MASLD风险的预测模型LMPM具有良好的预测性能，对及时识别高危人群有一定的指导意义。

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陈文生，E-mail：
CHEN Wensheng，E-mail：
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Int J Gen Med，2021，14：2909-2922., articleTitle=Application of a novel prediction model for predicting 2-year risk of non-alcoholic fatty liver disease in the non-obese population with normal blood lipid levels：a large prospective cohort study from China, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1322210981653103350, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210979845358311, xref=1, ext=[AuthorCompanyExt(id=1322210981665686263, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210979845358311, companyId=1322210981653103350, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1Department of Gastroenterology，First Affiliated Hospital，Army Medical University（Third Military Medical University），Chongqing，China), AuthorCompanyExt(id=1322210981674074872, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210979845358311, companyId=1322210981653103350, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1陆军军医大学（第三军医大学）第一附属医院消化内科，重庆)])], figs=[ArticleFig(id=1322210983297270547, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210979845358311, language=EN, label=null, caption=null, figureFileSmall=GRtQe1MQ6h/H65csKCqUzw==, figureFileBig=FY7gkgUSCSVJPsZ+rof7Gw==, tableContent=null), ArticleFig(id=1322210983372768020, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210979845358311, language=CN, label=图1, caption=LASSO回归筛选瘦型MASLD相关变量

A：LASSO回归系数路径图；B：LASSO回归交叉验证结果

, figureFileSmall=GRtQe1MQ6h/H65csKCqUzw==, figureFileBig=FY7gkgUSCSVJPsZ+rof7Gw==, tableContent=null), ArticleFig(id=1322210983561511701, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210979845358311, language=EN, label=null, caption=null, figureFileSmall=SjfHrJjiPLXA0xJZw7mksw==, figureFileBig=xqb+cVhGtOzzZN8q8d4J8A==, tableContent=null), ArticleFig(id=1322210983624426262, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210979845358311, language=CN, label=图2, caption=评估瘦型MASLD风险的列线图预测模型, figureFileSmall=SjfHrJjiPLXA0xJZw7mksw==, figureFileBig=xqb+cVhGtOzzZN8q8d4J8A==, tableContent=null), ArticleFig(id=1322210983716700951, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210979845358311, language=EN, label=null, caption=null, figureFileSmall=F6CVEbbV1CdtpOmlfWHqYw==, figureFileBig=SoZci0I6OCjE/2wHohNGAQ==, tableContent=null), ArticleFig(id=1322210983813169944, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210979845358311, language=CN, label=图3, caption=LMPM、FLI和HSI在训练集（A）和验证集（B）中的ROC曲线, figureFileSmall=F6CVEbbV1CdtpOmlfWHqYw==, figureFileBig=SoZci0I6OCjE/2wHohNGAQ==, tableContent=null), ArticleFig(id=1322210983884473113, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210979845358311, language=EN, label=null, caption=null, figureFileSmall=PW5Z0Mu7WQ3BFE9I0RwbPg==, figureFileBig=wXm0OOHLGWMs+48RnMJZUg==, tableContent=null), ArticleFig(id=1322210983964164890, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210979845358311, language=CN, label=图4, caption=LMPM在训练集（A）和验证集（B）中的校准曲线, figureFileSmall=PW5Z0Mu7WQ3BFE9I0RwbPg==, figureFileBig=wXm0OOHLGWMs+48RnMJZUg==, tableContent=null), ArticleFig(id=1322210984035468059, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210979845358311, language=EN, label=null, caption=null, figureFileSmall=PUWTeppKgEEcuB7gd0PReQ==, figureFileBig=va4HGnwMXZUUuhDnYkp7Kg==, tableContent=null), ArticleFig(id=1322210984115159836, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210979845358311, language=CN, label=图5, caption=LMPM、FLI和HSI在训练集（A）和验证集（B）中的DCA, figureFileSmall=PUWTeppKgEEcuB7gd0PReQ==, figureFileBig=va4HGnwMXZUUuhDnYkp7Kg==, tableContent=null), ArticleFig(id=1322210984178074397, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210979845358311, language=EN, label=null, caption=null, figureFileSmall=i0LsRdH9qqxHcoiSBmOdiw==, figureFileBig=HCZ78rEWtJJPE8dDnZAENg==, tableContent=null), ArticleFig(id=1322210984240988958, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210979845358311, language=CN, label=图6, caption=LMPM在训练集（A）和验证集（B）中的CIC, figureFileSmall=i0LsRdH9qqxHcoiSBmOdiw==, figureFileBig=HCZ78rEWtJJPE8dDnZAENg==, tableContent=null), ArticleFig(id=1322210984316486431, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210979845358311, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
指标总人群（n = 1 123）非MASLD组（n = 882）MASLD组（n = 241）P
年龄/岁47.63±21.1144.08±20.9560.63±15.98<0.001
性别0.879
男性531（47.28）416（47.17）115（47.72）
女性592（52.72）466（52.83）126（52.28）
种族<0.001
非西班牙裔黑人292（26.00）258（29.25）34（14.11）
非西班牙裔白人381（33.93）276（31.29）105（43.57）
其他西班牙裔94（8.37）77（8.73）17（7.05）
非西班牙裔亚洲人215（19.15）161（18.25）54（22.41）
墨西哥裔美国人85（7.57）66（7.48）19（7.88）
其他种族56（4.99）44（4.99）12（4.98）
臂围/cm27.78±2.7827.53±2.8428.69±2.30<0.001
WC/cm81.20±7.9579.56±7.5087.20±6.57<0.001
臀围/cm92.95±5.5392.51±5.6094.53±4.97<0.001
腰臀比0.87±0.070.86±0.070.92±0.06<0.001
腰高比0.49±0.050.48±0.050.53±0.04<0.001
BMI/（kg/m2）21.85±2.0721.56±2.1022.91±1.51<0.001
吸烟状态0.001
从不吸烟801（71.33）637（72.22）164（68.05）
曾经吸烟169（15.05）116（13.15）53（21.99）
现在吸烟153（13.62）129（14.63）24（9.96）
高血压<0.001
否801（71.33）677（76.76）124（51.45）
是322（28.67）205（23.24）117（48.55）
糖尿病<0.001
否1 019（90.74）827（93.76）192（79.67）
是104（9.26）55（6.24）49（20.33）
空腹血糖/（mmol/L）5.29±1.695.13±1.445.86±2.33<0.001
糖化血红蛋白/%5.58±0.875.48±0.685.95±1.30<0.001
总胆红素/（μmol/L）8.81±5.428.87±5.448.57±5.330.119
总蛋白/（g/L）72.00±4.4072.07±4.4571.71±4.250.438
白蛋白/（g/L）41.74±3.2041.77±3.2341.61±3.100.472
球蛋白/（g/L）30.26±4.1430.30±4.2330.10±3.790.657
ALT/（IU/L）17.07±15.0116.66±16.0118.56±10.51<0.001
AST/（IU/L）20.66±10.5120.66±11.3520.66±6.610.062
ALT/AST0.81±0.250.80±0.240.88±0.27<0.001
碱性磷酸酶/（IU/L）73.63±23.2472.07±22.0779.34±26.39<0.001
GGT/（IU/L）21.22±19.8420.27±18.7124.70±23.22<0.001
总胆固醇/（mmol/L）4.71±1.054.61±0.975.06±1.22<0.001
TG/（mmol/L）1.16±0.661.04±0.471.60±0.99<0.001
HDL-C/（mmol/L）1.56±0.381.58±0.361.48±0.41<0.001
肌酐/（μmol/L）78.55±42.6377.95±36.3080.76±60.440.854
尿素/（mmol/L）5.28±2.125.16±2.085.75±2.24<0.001
尿酸/（μmol/L）285.07±74.11279.77±70.52304.47±83.28<0.001
血红蛋白数目/（g/dL）13.82±1.4413.78±1.4113.99±1.540.045
白细胞数目/（×109/L）6.57±2.846.48±3.016.87±2.090.003
淋巴细胞数目/（×109/L）2.00±2.011.98±2.242.08±0.680.002
单核细胞数目/（×109/L）0.53±0.200.52±0.200.55±0.190.011
中性粒细胞数目/（×109/L）3.82±1.653.77±1.644.02±1.660.017
FLI15.24±13.4312.24±10.5426.21±16.77<0.001
HSI29.60±3.2329.10±3.1331.41±2.92<0.001
), ArticleFig(id=1322210984383595296, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210979845358311, language=CN, label=表1, caption=

1 123例瘦型参与者的基线特征［x¯±s，例（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
指标总人群（n = 1 123）非MASLD组（n = 882）MASLD组（n = 241）P
年龄/岁47.63±21.1144.08±20.9560.63±15.98<0.001
性别0.879
男性531（47.28）416（47.17）115（47.72）
女性592（52.72）466（52.83）126（52.28）
种族<0.001
非西班牙裔黑人292（26.00）258（29.25）34（14.11）
非西班牙裔白人381（33.93）276（31.29）105（43.57）
其他西班牙裔94（8.37）77（8.73）17（7.05）
非西班牙裔亚洲人215（19.15）161（18.25）54（22.41）
墨西哥裔美国人85（7.57）66（7.48）19（7.88）
其他种族56（4.99）44（4.99）12（4.98）
臂围/cm27.78±2.7827.53±2.8428.69±2.30<0.001
WC/cm81.20±7.9579.56±7.5087.20±6.57<0.001
臀围/cm92.95±5.5392.51±5.6094.53±4.97<0.001
腰臀比0.87±0.070.86±0.070.92±0.06<0.001
腰高比0.49±0.050.48±0.050.53±0.04<0.001
BMI/（kg/m2）21.85±2.0721.56±2.1022.91±1.51<0.001
吸烟状态0.001
从不吸烟801（71.33）637（72.22）164（68.05）
曾经吸烟169（15.05）116（13.15）53（21.99）
现在吸烟153（13.62）129（14.63）24（9.96）
高血压<0.001
否801（71.33）677（76.76）124（51.45）
是322（28.67）205（23.24）117（48.55）
糖尿病<0.001
否1 019（90.74）827（93.76）192（79.67）
是104（9.26）55（6.24）49（20.33）
空腹血糖/（mmol/L）5.29±1.695.13±1.445.86±2.33<0.001
糖化血红蛋白/%5.58±0.875.48±0.685.95±1.30<0.001
总胆红素/（μmol/L）8.81±5.428.87±5.448.57±5.330.119
总蛋白/（g/L）72.00±4.4072.07±4.4571.71±4.250.438
白蛋白/（g/L）41.74±3.2041.77±3.2341.61±3.100.472
球蛋白/（g/L）30.26±4.1430.30±4.2330.10±3.790.657
ALT/（IU/L）17.07±15.0116.66±16.0118.56±10.51<0.001
AST/（IU/L）20.66±10.5120.66±11.3520.66±6.610.062
ALT/AST0.81±0.250.80±0.240.88±0.27<0.001
碱性磷酸酶/（IU/L）73.63±23.2472.07±22.0779.34±26.39<0.001
GGT/（IU/L）21.22±19.8420.27±18.7124.70±23.22<0.001
总胆固醇/（mmol/L）4.71±1.054.61±0.975.06±1.22<0.001
TG/（mmol/L）1.16±0.661.04±0.471.60±0.99<0.001
HDL-C/（mmol/L）1.56±0.381.58±0.361.48±0.41<0.001
肌酐/（μmol/L）78.55±42.6377.95±36.3080.76±60.440.854
尿素/（mmol/L）5.28±2.125.16±2.085.75±2.24<0.001
尿酸/（μmol/L）285.07±74.11279.77±70.52304.47±83.28<0.001
血红蛋白数目/（g/dL）13.82±1.4413.78±1.4113.99±1.540.045
白细胞数目/（×109/L）6.57±2.846.48±3.016.87±2.090.003
淋巴细胞数目/（×109/L）2.00±2.011.98±2.242.08±0.680.002
单核细胞数目/（×109/L）0.53±0.200.52±0.200.55±0.190.011
中性粒细胞数目/（×109/L）3.82±1.653.77±1.644.02±1.660.017
FLI15.24±13.4312.24±10.5426.21±16.77<0.001
HSI29.60±3.2329.10±3.1331.41±2.92<0.001
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变量训练集（n=561）验证集（n=562）P
年龄/岁48.30±21.2446.97±20.970.301
性别0.101
男性279（49.73）252（44.84）
女性282（50.27）310（55.16）
种族0.321
非西班牙裔黑人147（26.20）145（25.80）
非西班牙裔白人193（34.40）188（33.45）
其他西班牙裔38（6.77）56（9.96）
非西班牙裔亚洲人117（20.86）98（17.44）
墨西哥裔美国人39（6.95）46（8.19）
其他种族27（4.81）29（5.16）
臂围/cm27.81±2.6627.74±2.880.616
WC/cm81.41±7.9180.99±8.000.366
臀围/cm93.07±5.2792.82±5.780.457
腰臀比0.87±0.070.87±0.070.661
腰高比0.49±0.050.49±0.050.909
BMI/（kg/m2）21.89±2.0421.80±2.090.494
吸烟状态0.956
从不吸烟398（70.94）403（71.71）
曾经吸烟86（15.33）83（14.77）
现在吸烟77（13.73）76（13.52）
高血压0.910
否401（71.48）400（71.17）
是160（28.52）162（28.83）
糖尿病0.405
否505（90.02）514（91.46）
是56（9.98）48（8.54）
空腹血糖/（mmol/L）5.29±1.405.29±1.950.546
糖化血红蛋白/%5.59±0.885.57±0.870.466
总胆红素/（μmol/L）8.96±5.428.65±5.420.178
总蛋白/（g/L）72.03±4.6171.96±4.190.936
白蛋白/（g/L）41.79±3.1841.69±3.230.794
球蛋白/（g/L）30.25±4.2530.27±4.040.853
ALT/（IU/L）17.60±19.3116.54±8.830.227
AST/（IU/L）20.70±10.6520.62±10.380.522
ALT/AST0.82±0.260.80±0.240.132
碱性磷酸酶/（IU/L）73.16±23.0574.11±23.450.371
GGT/（IU/L）21.86±22.2720.58±17.070.666
总胆固醇/（mmol/L）4.71±1.074.71±1.020.708
TG/（mmol/L）1.16±0.651.15±0.680.576
HDL-C/（mmol/L）1.56±0.381.57±0.370.447
肌酐/（μmol/L）78.28±34.6378.82±49.370.215
尿素/（mmol/L）5.34±2.045.23±2.210.126
尿酸/（μmol/L）288.11±74.67282.03±73.480.125
血红蛋白数目/（g/dL）13.87±1.5113.77±1.370.164
白细胞数目/（×109/L）6.63±3.536.50±1.920.504
淋巴细胞数目/（×109/L）2.07±2.781.93±0.600.647
单核细胞数目/（×109/L）0.53±0.200.52±0.190.521
中性粒细胞数目/（×109/L）3.81±1.673.83±1.620.495
FLI15.53±13.4014.95±13.460.281
HSI29.69±3.2829.50±3.170.304
), ArticleFig(id=1322210984589116194, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210979845358311, language=CN, label=表2, caption=

训练集和验证集的基线特征［x¯±s，例（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
变量训练集（n=561）验证集（n=562）P
年龄/岁48.30±21.2446.97±20.970.301
性别0.101
男性279（49.73）252（44.84）
女性282（50.27）310（55.16）
种族0.321
非西班牙裔黑人147（26.20）145（25.80）
非西班牙裔白人193（34.40）188（33.45）
其他西班牙裔38（6.77）56（9.96）
非西班牙裔亚洲人117（20.86）98（17.44）
墨西哥裔美国人39（6.95）46（8.19）
其他种族27（4.81）29（5.16）
臂围/cm27.81±2.6627.74±2.880.616
WC/cm81.41±7.9180.99±8.000.366
臀围/cm93.07±5.2792.82±5.780.457
腰臀比0.87±0.070.87±0.070.661
腰高比0.49±0.050.49±0.050.909
BMI/（kg/m2）21.89±2.0421.80±2.090.494
吸烟状态0.956
从不吸烟398（70.94）403（71.71）
曾经吸烟86（15.33）83（14.77）
现在吸烟77（13.73）76（13.52）
高血压0.910
否401（71.48）400（71.17）
是160（28.52）162（28.83）
糖尿病0.405
否505（90.02）514（91.46）
是56（9.98）48（8.54）
空腹血糖/（mmol/L）5.29±1.405.29±1.950.546
糖化血红蛋白/%5.59±0.885.57±0.870.466
总胆红素/（μmol/L）8.96±5.428.65±5.420.178
总蛋白/（g/L）72.03±4.6171.96±4.190.936
白蛋白/（g/L）41.79±3.1841.69±3.230.794
球蛋白/（g/L）30.25±4.2530.27±4.040.853
ALT/（IU/L）17.60±19.3116.54±8.830.227
AST/（IU/L）20.70±10.6520.62±10.380.522
ALT/AST0.82±0.260.80±0.240.132
碱性磷酸酶/（IU/L）73.16±23.0574.11±23.450.371
GGT/（IU/L）21.86±22.2720.58±17.070.666
总胆固醇/（mmol/L）4.71±1.074.71±1.020.708
TG/（mmol/L）1.16±0.651.15±0.680.576
HDL-C/（mmol/L）1.56±0.381.57±0.370.447
肌酐/（μmol/L）78.28±34.6378.82±49.370.215
尿素/（mmol/L）5.34±2.045.23±2.210.126
尿酸/（μmol/L）288.11±74.67282.03±73.480.125
血红蛋白数目/（g/dL）13.87±1.5113.77±1.370.164
白细胞数目/（×109/L）6.63±3.536.50±1.920.504
淋巴细胞数目/（×109/L）2.07±2.781.93±0.600.647
单核细胞数目/（×109/L）0.53±0.200.52±0.190.521
中性粒细胞数目/（×109/L）3.81±1.673.83±1.620.495
FLI15.53±13.4014.95±13.460.281
HSI29.69±3.2829.50±3.170.304
), ArticleFig(id=1322210984668807971, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210979845358311, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
变量估计值标准误ZP比值比95%CI
年龄0.020.013.36<0.0011.021.01~1.04
WC0.110.026.05<0.0011.121.08~1.16
TG1.480.236.56<0.0014.392.82~6.82
), ArticleFig(id=1322210984756888356, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210979845358311, language=CN, label=表3, caption=

多因素Logistic回归分析筛选独立危险因素

, figureFileSmall=null, figureFileBig=null, tableContent=
变量估计值标准误ZP比值比95%CI
年龄0.020.013.36<0.0011.021.01~1.04
WC0.110.026.05<0.0011.121.08~1.16
TG1.480.236.56<0.0014.392.82~6.82
), ArticleFig(id=1322210984840774437, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210979845358311, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
预测模型AUCP最佳截点灵敏度特异度PPVNPVNRIIDI
训练集
LMPM0.86（0.82~0.89）Ref0.2080.95%73.33%0.470.93RefRef
FLI0.83（0.79~0.87）0.02215.6176.19%76.09%0.480.920.17（0.09，0.25）0.05（0.02，0.08）
HSI0.71（0.66~0.76）<0.00129.2882.54%52.87%0.340.91-0.11（-0.23，0.01）0.23（0.18，0.28）
验证集
LMPM0.81（0.77~0.85）Ref0.2077.39%71.36%0.410.92RefRef
FLI0.74（0.70~0.79）<0.00110.9578.26%58.84%0.330.910.31（0.21，0.40）0.08（0.06，0.11）
HSI0.71（0.65~0.76）<0.00129.9372.17%62.19%0.330.90-0.01（-0.13，0.12）0.11（0.06，0.15）
), ArticleFig(id=1322210984920466214, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210979845358311, language=CN, label=表4, caption=

训练集和验证集中LMPM、FLI和HSI预测性能的比较

, figureFileSmall=null, figureFileBig=null, tableContent=
预测模型AUCP最佳截点灵敏度特异度PPVNPVNRIIDI
训练集
LMPM0.86（0.82~0.89）Ref0.2080.95%73.33%0.470.93RefRef
FLI0.83（0.79~0.87）0.02215.6176.19%76.09%0.480.920.17（0.09，0.25）0.05（0.02，0.08）
HSI0.71（0.66~0.76）<0.00129.2882.54%52.87%0.340.91-0.11（-0.23，0.01）0.23（0.18，0.28）
验证集
LMPM0.81（0.77~0.85）Ref0.2077.39%71.36%0.410.92RefRef
FLI0.74（0.70~0.79）<0.00110.9578.26%58.84%0.330.910.31（0.21，0.40）0.08（0.06，0.11）
HSI0.71（0.65~0.76）<0.00129.9372.17%62.19%0.330.90-0.01（-0.13，0.12）0.11（0.06，0.15）
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瘦型代谢功能障碍相关脂肪性肝病风险预测模型的构建与验证
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秦嘉怡 , 周学谦 , 孟祥勇 , 陈文生
陆军军医大学学报 | 临床医学 2025,47(9): 969-979
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陆军军医大学学报 |临床医学 2025 , 47 (9) : 969 -979
瘦型代谢功能障碍相关脂肪性肝病风险预测模型的构建与验证
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秦嘉怡, 周学谦, 孟祥勇, 陈文生
作者信息
  • 1陆军军医大学（第三军医大学）第一附属医院消化内科，重庆
通讯作者:
陈文生，E-mail：
CHEN Wensheng，E-mail：
Construction and validation of a risk prediction model for lean metabolic dysfunction-associated steatotic liver disease
Jiayi QIN, Xueqian ZHOU, Xiangyong MENG, Wensheng CHEN
Affiliations
  • 1Department of Gastroenterology，First Affiliated Hospital，Army Medical University（Third Military Medical University），Chongqing，China
出版时间: 2025-05-15 doi: 10.16016/j.2097-0927.202412024
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摘要
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目的

探讨瘦型人群发生代谢功能障碍相关脂肪性肝病（metabolic dysfunction-associated steatotic liver disease，MASLD）的危险因素，并构建风险预测模型。

方法

本研究基于美国国家健康和营养检查调查数据库（2017年1月至2020年3月），纳入1 123名成年个体。通过简单随机抽样将其分为训练集（n=561）和验证集（n=562），并收集有关人口统计学、人体测量学、生活方式、基础疾病和实验室检查结果的数据。在训练集中运用LASSO回归筛选潜在变量，再通过多因素Logistic回归确定瘦型MASLD的独立危险因素。基于这些危险因素，构建瘦型MASLD预测模型（prediction model for lean metabolic dysfunction-associated steatotic liver disease，LMPM）。为评估 LMPM的临床价值，将其与脂肪肝指数（fatty liver index，FLI）和肝脂肪变性指数（hepatic steatosis index，HSI）这两种常用的非酒精性脂肪性肝病预测模型进行比较。通过受试者工作特征曲线下面积（area under the receiver operating characteristic curve，AUC）、净重新分类指数（net reclassification index，NRI）、综合判别改善指数（integrated discrimination improvement，IDI）、校准曲线、决策曲线和临床影响曲线对模型的性能进行评价和内部验证。

结果

本研究确定年龄、腰围和甘油三酯为瘦型人群发生MASLD的独立危险因素。基于上诉指标建立的LMPM在训练集和验证集中表现出良好的区分能力，AUC值分别为0.86（95%CI：0.82~0.89）和0.81（95%CI：0.77~0.85），明显优于FLI［训练集AUC=0.83（95%CI：0.79~0.87）；验证集AUC=0.74（95%CI：0.70~0.79）］和HSI［训练集AUC=0.71（95%CI：0.66~0.76）；验证集AUC=0.71（95%CI：0.65~0.76）］。相较于FLI和HSI，LMPM在训练集和验证集中NRI和IDI均得到改善。校准曲线显示出LMPM的高准确性，决策曲线和临床影响曲线均表明LMPM提供了更大的临床益处。

结论

年龄、腰围和甘油三酯是瘦型MASLD的独立危险因素，据此构建的用于评估瘦型人群发生MASLD风险的预测模型LMPM具有良好的预测性能，对及时识别高危人群有一定的指导意义。

瘦型代谢功能障碍相关脂肪性肝病  /  危险因素  /  预测模型
Objective‍

To investigate the risk factors for the occurrence of metabolic dysfunction-associated steatotic liver disease(MASLD) in lean individuals and construct a risk prediction model.

Methods‍

Based on the National Health and Nutrition Examination Survey(NHANES) database in the United States(from January 2017 to March 2020), 1 123 adult individuals were included in this study. Then the participants were randomly divided into a training set(n=561) and a validation set(n=562) through simple random sampling. Data on their demographics, anthropometrics, lifestyle, underlying diseases, and laboratory test results were collected. LASSO regression analysis was used to screen potential variables in the training set, and multivariate logistic regression was employed to identify independent risk factors for lean MASLD. Based on these risk factors, a prediction model for lean MASLD was constructed(LMPM). To evaluate the clinical value of the LMPM, it was compared with two commonly used prediction models for non-alcoholic fatty liver disease, the fatty liver index(FLI) and the hepatic steatosis index(HSI). The performance of the model was evaluated and internally validated using the area under the receiver operating characteristic curve(AUC), net reclassification index(NRI), integrated discrimination improvement(IDI), calibration curve, decision curve, and clinical impact curve.

Results‍

Age, waist circumference, and triglycerides(TG) were identified as independent risk factors for the development of MASLD in lean individuals. The LMPM, constructed based on these indicators, demonstrated good discriminative ability in both the training and validation sets, with AUC values of 0.86(95%CI: 0.82~0.89) and 0.81(95%CI: 0.77~0.85), respectively, which were significantly better than those of FLI [training set: AUC=0.83(95%CI: 0.79~0.87); validation set: AUC=0.74(95%CI: 0.70~0.79)] and HSI [training set: AUC=0.71(95%CI: 0.66~0.76); validation set: AUC=0.71(95%CI: 0.65~0.76)]. Compared with FLI and HSI, the LMPM showed improvements in NRI and IDI in both the training and validation sets. The calibration curve demonstrated its high accuracy, and both the decision curve analysis and the clinical impact curve analysis indicated that the LMPM provided greater clinical benefits.

Conclusion‍

Age, waist circumference, and TG are independent risk factors for lean MASLD. Based on these factors, a prediction model, named LMPM, is developed to assess the risk of MASLD in lean individuals, which exhibits good predictive performance and has certain guiding significance for the timely identification of high-risk populations.

lean metabolic dysfunction-associated steatotic liver disease  /  risk factors  /  prediction model
秦嘉怡, 周学谦, 孟祥勇, 陈文生. 瘦型代谢功能障碍相关脂肪性肝病风险预测模型的构建与验证. 陆军军医大学学报, 2025 , 47 (9) : 969 -979 . DOI: 10.16016/j.2097-0927.202412024
Jiayi QIN, Xueqian ZHOU, Xiangyong MENG, Wensheng CHEN. Construction and validation of a risk prediction model for lean metabolic dysfunction-associated steatotic liver disease[J]. Journal of Army Medical University, 2025 , 47 (9) : 969 -979 . DOI: 10.16016/j.2097-0927.202412024
正文
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非酒精性脂肪性肝病（non-alcoholic fatty liver disease，NAFLD）在2023年更名为代谢功能障碍相关脂肪性肝病（metabolic dysfunction-associated steatotic liver disease，MASLD），全球患病率为25%~45%，是目前最常见的慢性肝病［1-2］。大多数MASLD患者在早期没有症状，这限制了疾病的早期发现和预防。MASLD并不是一种良性的静态肝病。一方面，MASLD是导致肝硬化和肝癌发展的主要因素之一［3］。另一方面，MASLD还会引发各种肝外表现，包括肝外癌症、糖尿病、心血管疾病和慢性肾病等［4］。鉴于此，MASLD已成为全球许多国家和地区肝病负担的主要原因，是21世纪的重大全球公共卫生挑战［5］。
MASLD通常与超重和肥胖相关［6］。然而，MASLD患者并非都患有超重或肥胖症，全球约40%的MASLD患者被归类为非肥胖型，大约20%的MASLD患者的体质指数（body mass index，BMI）低于超重阈值（欧　美人群BMI<25 kg/m²，亚洲人群BMI<23 kg/m²），称为瘦型MASLD［7］。与超重或肥胖患者相比，瘦型MASLD患者可能面临更差的预后，其肝病进展、慢性肾病的发生情况更严重，总体死亡率也更高［8-10］。然而，目前并没有针对瘦型MASLD患者的具体治疗策略。MASLD的早期阶段可以通过健康的生活方式来逆转［11］，因此，有必要对MASLD患者进行早期识别、诊断和生活方式干预。诊断MASLD的金标准是肝活检，但由于其侵入性和并发症风险而受到限制［12］。此外，常见的影像学技术，如腹部多普勒超声，由于其相对较高的成本，并不适合大规模筛查［13］。因此，迫切需要可靠、简单且非侵入性的评分系统来弥补这些局限。
目前已经开发了几种预测NAFLD发生概率的无创模型，其中脂肪肝指数（fatty liver index，FLI）和肝脂肪变性指数（hepatic steatosis index，HSI）是使用最广泛的两种模型，并且FLI对正常体质量人群中的脂肪肝也具有中度预测能力［14-16］。尽管有研究显示NAFLD与MASLD患者群体大多重叠［17］，但对于体型偏瘦的人群而言，情况可能并非如此，因为这类瘦型患者不满足BMI诊断标准，大约有10%的瘦型NAFLD会被归类为隐源性脂肪性肝病［18］。因此，探究针对瘦型MASLD的新型非侵入性预测模型是必要的。列线图作为一种直观的、易于使用的风险预测模型，已成为多种疾病早期识别和预警的工具［19-20］，而目前缺乏针对瘦型MASLD风险的列线图。因此，本研究利用常见实验室检测指标创建并验证一个针对瘦型人群发生MASLD风险的列线图预测模型，并将新模型与FLI和HSI模型进行比较，旨在提供一种更加便捷且准确的方法来识别瘦型MASLD的高风险个体，为临床决策提供更加有力的理论支持。
1　资料与方法
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1.1　数据来源与研究对象
美国国家健康和营养检查调查（national health and nutrition examination survey，NHANES）数据库是一项全国性的横断面调查数据库，旨在全面评估美国人群的健康与营养状况。该数据库采用多阶段分层概率抽样方法，确保了样本的高度代表性，在数据收集之前，所有研究程序已通过国家卫生统计中心研究伦理审查委员会的批准。所有参与者根据NHANES协议签署了书面知情同意书，明确表示愿意参与此次调查。本研究涉及的数据已在网站（https://www.cdc.gov/nchs/nhanes）上公开发布，供公众查阅。我们以数据库中2017年1月至2020年3月的人群为研究对象。初始队列包括15 560名参与者，根据以下标准排除：①肝脏瞬时弹性成像测量、BMI、年龄等基线资料不完整；②存在乙型或丙型肝炎和酗酒；③年龄<18岁；④超重和肥胖。最终纳入1 123名参与者。
1.2　观察指标
收集既往文献报道的与MASLD关系密切的指标［13］。具体包括人口统计学指标：年龄、性别和种族；人体测量指标：臂围、腰围（waist circumference，WC）、臀围、腰臀比、腰高比、BMI；生活方式指标：吸烟状态；基础疾病：高血压和糖尿病；实验室指标：空腹血糖、糖化血红蛋白、总胆红素、总蛋白、白蛋白、球蛋白、谷丙转氨酶（alanine aminotransferase，ALT）、谷草转氨酶（aspartate aminotransferase，AST）、ALT/AST、碱性磷酸酶、谷氨酰基转移酶（gamma-glutamyl transferase，GGT）、总胆固醇、甘油三酯（triglycerides，TG）、高密度脂蛋白胆固醇（high-density lipoprotein cholesterol，HDL-C）、肌酐、尿素、尿酸、血红蛋白数目、白细胞数目、淋巴细胞数目、单核细胞数目、中性粒细胞数目。
1.3　诊断标准
MASLD的诊断需满足以下条件：经超声确认肝脏脂肪变性且存在至少1种心脏代谢危险因素，并排除大量饮酒史或其他慢性肝病［2］。肝脏脂肪变性通过振动控制瞬时弹性成像技术获得的受控衰减参数（controlled attenuation parameter，CAP）进行识别，当CAP≥238 dB/m可判定为存在肝脏脂肪变性［21］。在本研究中，瘦型MASLD为瘦型个体所患有的MASLD［10］。吸烟状态包括：从不吸烟、曾经吸烟和现在吸烟。吸烟总数少于100支为从不吸烟，过去吸烟但目前已戒烟为曾经吸烟，目前仍有吸烟行为则为现在吸烟。高血压的诊断标准：①高血压病史；②正在接受降压治疗；③收缩压≥140 mmHg和/或舒张压≥90 mmHg［22］。糖尿病的诊断标准：①糖尿病病史；②正在接受降糖治疗；③空腹血糖≥7.0 mmol/L；④糖化血红蛋白≥6.5%［23］。
FLI和HSI是用于在没有影像学或活检数据的情况下评估肝脏脂肪变性的无创生物标志物，旨在帮助识别可能患有NAFLD的患者。NAFLD定义为在没有其他脂肪变性原因（例如病毒性肝炎和过量饮酒）的情况下存在肝脏脂肪变性［24］。FLI是由BEDOGNI等［14］于2006年在意大利创立，可以根据TG、BMI、GGT和WC的公式进行计算：FLI=e［0.953×ln（TG）+0.139×BMI+0.718×ln（GGT）+0.053×WC-15.745］/（1+e［0.953×ln（TG）+ 0.139×BMI+0.718×ln（GGT）+0.053×WC-15.745］）×100，当FLI≥30且参与者没有其他已确定的慢性肝病危险因素即可诊断NAFLD［25］。HSI是由LEE等［15］于2010在韩国建立，其可由以下公式计算得出：HSI=8×（ALT/AST）+BMI+2（如果为2型糖尿病患者）+2（如果为女性）。根据先前验证的临界值，当HSI>36即可认为参与者患有NAFLD。
1.4　统计学分析
所有的数据分析通过RStudio 4.3.2和易侕统计（4.0）完成，P<0.05表示差异具有统计学意义。根据是否患有MASLD将研究对象分为2组，比较其性别、年龄、种族等各项指标。符合正态分布的计量资料以x¯±s表示，并通过t检验来比较不同组别间的差异；不符合正态分布的计量资料，则以M（P25，P75）表示，组间差异采用Mann-Whitney U检验。计数资料则以例（%）表示，并利用χ²检验或Fisher确切概率来评估组间差异。采用LASSO回归和Logistic回归两种分析方法探讨观察指标与瘦型MASLD之间的关系。考虑到瘦型MASLD阳性事件数有限，同时为保障初始高维候选变量筛选流程的稳定性，本研究采用1∶1的比例对训练集和验证集进行分配，从而更全面地评估模型在不同样本上的性能，提高验证集统计效力和模型的泛化能力［26］。通过简单随机抽样将样本分为训练集（n=561）和验证集（n=562），分别用于模型构建与验证。采用R软件中的“glmnet”包进行LASSO回归，从多重共线性变量中筛选对因变量具有显著预测效力的特征变量；基于筛选出的非零系数变量构建Logistic回归模型。通过“rms”包整合Logistic回归中具有统计学意义的变量，建立瘦型MASLD预测模型（prediction model for lean metabolic dysfunction-associated steatotic liver disease，LMPM），并以列线图可视化。在训练集和验证集中分别计算各个模型的受试者工作特征曲线下面积（area under the receiver operating characteristic curve，AUC）、灵敏度、特异度、最佳截点、阳性预测值（positive predictive value，PPV）和阴性预测值（negative predictive value，NPV）进行对比分析；为弥补AUC对模型小幅改进敏感性不足的局限，补充计算净重新分类指数（net reclassification index，NRI）和综合判别改善指数（integrated discrimination improvement，IDI）以评估LMPM较FLI和HSI在风险分类准确性和概率预测效能上的提升幅度。通过绘制校准曲线联合Hosmer-Lemeshow检验评估LMPM的校准性能，采用决策曲线分析（decision curve analysis，DCA）量化不同模型的临床净获益，并绘制临床影响曲线（clinical impact curve，CIC）直观呈现模型在不同风险阈值下的表现差异。
2　结果
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2.1　参与者的基线特征
1 123名瘦型参与者的平均年龄为（47.63±21.11）岁，其中男性占47.28%。241名参与者被诊断为瘦型MASLD，患病率为21.46%，其中患病组的平均年龄显著高于健康对照组［（60.63±15.98） vs （44.08±20.95）岁，P<0.001，表1］。训练集与验证集2组人群具有相似的人口统计学指标和实验室检查指标（P>0.05，表2），说明数据集划分后组间均衡性良好，具有可比性。
2.2　变量选择与列线图构建
将训练集中收集的34个变量纳入LASSO回归分析。结果显示，最优模型中的非零系数变量分别是：年龄、WC、腰高比、TG（图1）。将LASSO回归筛选出的具有非零系数的变量纳入多因素Logistic回归分析。结果显示，除腰高比以外，其余变量均是瘦型MASLD的独立危险因素（P<0.001，表3）。基于上述发现，本研究构建了列线图预测模型LMPM，该模型专门用于评估瘦型人群发生MASLD的风险。例如，一位70岁的男性，WC为97.10 cm、TG浓度为0.84 mmol/L，最后3个预测变量得分的总和为89.90分，表示其患MASLD的概率为51.00%（图2）。
2.3　模型的性能评估
2.3.1　区分能力
为了评估模型的预测能力，本研究绘制了各个模型的受试者工作特征曲线（receiver operating characteristic curve，ROC）（图3），并分别计算了各个模型的AUC值、灵敏度、特异度及其他性能指标（表4）。在训练集中，LMPM的AUC为0.86（95%CI：0.82~0.89），高于FLI［0.83（95%CI：0.79~0.87）］和HSI［0.71（95%CI：0.66~0.76）］，且差异具有统计学意义（P<0.05），这表明LMPM在训练集中具有更优越的预测性能。LMPM在训练集中的灵敏度为80.95%，特异度为73.33%，PPV为0.47，NPV为0.93。验证集中也显示出类似的结果，其中LMPM的AUC为0.81（95%CI：0.77~0.85），显著高于FLI［0.74（95%CI：0.70~0.79）］和HSI［0.71（95%CI：0.65~0.76）］，且差异具有统计学意义（P<0.001）。在验证集中，LMPM的灵敏度为77.39%，特异度为71.36%，PPV为0.41，NPV为0.92。此外，NRI和IDI的改善表明，LMPM在风险分类性能和概率预测效能上优于FLI和HSI。
2.3.2　校准性能
图4展示了LMPM在训练集与验证集中的校准曲线。从图中可以直观看出，这2条校准曲线与理想的45°线紧密贴合。此外，Hosmer-Lemeshow检验的结果显示，无论是训练集（χ²=5.52，P=0.70）还是验证集（χ²=10.93，P=0.21），LMPM的预测结果与观察结果之间均不存在显著性差异。这充分证明LMPM经过了良好的校准，其预测概率与实际结果高度一致。
2.3.3　临床实用性
DCA显示，在较大的阈值范围内，LMPM的净收益高于FLI和HSI（图5），提示其临床适用性更优。进一步分析LMPM的CIC，结果显示，当风险阈值设定在0.6以上时，LMPM判定为瘦型MASLD的高风险人群与实际患病的人群高度匹配（图6）。这充分凸显了LMPM在临床应用中的有效率较高。
3　讨论
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瘦型MASLD患者早期症状轻微，临床生化指标较低，同时由于具有“欺骗性”的BMI值，导致其漏诊率较高，随后进展为严重肝病。因此，开发针对瘦型人群的预测工具对于疾病的预防和管理非常重要。现有的研究已建立了针对瘦型NAFLD的预测模型［27］。然而，鉴于NAFLD已更名为MASLD，这些模型尚未在瘦型MASLD人群中得到验证。因此，本研究分析了瘦型人群发生MASLD的危险因素，确定了年龄、WC和TG是瘦型MASLD的独立危险因素。基于这3个危险因素构建的列线图预测模型LMPM，相较于常用FLI和HSI，在预测准确性、辨别力以及临床效用方面表现更优。
3.1　瘦型MASLD的发病机制
瘦型MASLD发生的确切病理生理机制尚不清楚。以往的研究表明，基因与环境的相互作用、表观遗传学、代谢健康等在瘦型MASLD的发病机理中起着重要作用［11］。瘦型MASLD是一种具有遗传倾向的疾病，大量研究表明携带含Patatin样磷脂酶结构域的蛋白3 rs738409基因与瘦型MASLD的发生密切相关［28］。此外，营养状况、吸烟习惯、酒精摄入、年龄、运动和宫内环境等环境因素可能与个体的遗传背景发生相互作用，从而影响个体的代谢健康，进而促进疾病的发展。而表观基因组在MASLD的发展中可能发挥双重作用：它既是MASLD发生的诱因，又能促进基因与环境的相互作用［11］。MASLD 的发病受代谢健康状况的影响显著，若能在早期识别高危人群，并积极进行生活方式干预、减轻体质量，则能显著改善瘦型患者的肝脂肪变性及代谢损害［29］。
3.2　年龄对瘦型MASLD的影响
本研究发现，随着年龄的增长，瘦型人群患MASLD的风险也随之增加。以往研究表明，非肥胖型MASLD在65岁以上的人群中比在年轻人群中更常见，年龄是非肥胖个体发生MASLD的独立危险因素［30］。最近一篇对22项研究进行荟萃分析的文章也指出，瘦型MASLD患者比瘦型健康个体年长2.87岁［31］，这可能与代谢变化、共存疾病情况和缺乏身体活动等因素有关。尽管年龄不可逆转，但鼓励年长者积极参与定期健康筛查，有利于控制疾病的进展。
3.3　WC对瘦型MASLD的影响
在本研究中，WC也是预测瘦型个体发生MASLD的关键指标。WC作为衡量中心性肥胖的指标，反映了内脏脂肪的积累［32］。既往的研究表明，非肥胖型MASLD患者可能存在过多的内脏脂肪组织［33］，这与胰岛素抵抗、代谢紊乱和肝脂肪变性有关［34］，内脏肥胖可能是瘦型MASLD的一个关键危险因素［35］。对于BMI正常的人来说，轻微的体质量增加可能导致代谢紊乱和内脏脂肪增加［36］，且非肥胖型MASLD患者的内脏肥胖指数通常高于超重和肥胖型MASLD患者［37］，这表明低BMI的患者对于小幅体质量增加的代谢调节能力可能低于BMI较高的患者［38］。因此，相较于肥胖型患者，针对瘦型MASLD患者实施减少久坐时间、调整饮食结构等生活方式干预措施，可能会取得更好的效果［39］。
3.4　TG对瘦型MASLD的影响
与本研究结果一致，在目前的研究中，血脂异常，尤其是高TG血症是MASLD 发生、进展的重要因素，并且与瘦型MASLD相关［40-41］。血脂异常通过3种关键机制导致肝脏脂质的过度积累：内脏脂肪组织中的脂解作用增加、肝脏的新生脂肪生成（de novo lipogenesis，DNL）激活以及高脂饮食［42］。胰岛素抵抗（insulin resistance，IR）在肝脂肪变性的进展中起着核心作用，它通过作用于脂肪组织刺激游离脂肪酸（free fatty acid，FFA）的释放增加，进而促进TG合成。瘦型MASLD患者有更多的内脏脂肪积累，内脏脂肪组织比皮下脂肪组织具有更强的脂肪分解活性，这导致肝脏中高水平的FFA并加剧肝脏TG的积累［16］。TG水平升高会造成有害脂质的积累，特别是神经酰胺和二酰甘油，这进一步加剧了IR并触发炎症级联反应［27，43］。在MASLD中，IR还可以诱导肝脏TG的新生合成，显著提高DNL的速率，从而加剧脂肪肝的进展［44］。
3.5　LMPM对瘦型MASLD的预测价值
本研究基于具有全国代表性的大样本NHANES数据库，确保了结果的可靠性。同时，为了确保模型的普适性，本研究将纳入的指标限制为常规实验室检查参数，使其适用于各级医疗机构的早期筛查。研究结果显示，与现有的主流NAFLD模型相比，LMPM不仅展现出更优的预测效能和临床实用性，还通过显著减少预测指标数量（仅需3个基础参数），在降低筛查成本的同时提升临床筛查意愿，为瘦型MASLD的精准防控提供了高效的工具。
然而，本研究也存在一些局限性：①由于采用了横断面设计，只能识别相关性，无法确定变量与结果之间的直接因果关系；②MASLD的诊断基于肝脏超声检查而不是肝脏活检，脂肪变性的结果可能存在一定的误差，未来的研究应在可能的情况下将肝活检作为诊断标准，以确保诊断的准确性；③尽管该列线图在训练集和内部验证集中表现良好，但仍需在更大规模的外部验证集和前瞻性研究中评估模型的泛化能力。
综上所述，年龄、WC和TG是瘦型人群发生MASLD的独立危险因素。基于上述3项指标，本研究构建了列线图预测模型LMPM。与现有的主流NAFLD模型相比，LMPM表现出更优越的预测准确性和临床实用性，对及时识别高危人群有一定的指导意义。

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2025年第47卷第9期
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doi: 10.16016/j.2097-0927.202412024
  • 接收时间：2024-12-06
  • 首发时间：2026-10-28
  • 出版时间：2025-05-15
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  • 收稿日期：2024-12-06
  • 修回日期：2025-02-17
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    1陆军军医大学（第三军医大学）第一附属医院消化内科，重庆

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陈文生，E-mail：
CHEN Wensheng，E-mail：
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2种不同金属材料的力学参数
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Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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