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Article(id=1241022941144609579, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241022939957621542, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202409473, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1727280000000, receivedDateStr=2024-09-26, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773812526260, onlineDateStr=2026-03-18, pubDate=1742832000000, pubDateStr=2025-03-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773812526260, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773812526260, creator=13701087609, updateTime=1773812526260, updator=13701087609, issue=Issue{id=1241022939957621542, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='6', pageStart='961', pageEnd='1152', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773812525976, creator=13701087609, updateTime=1773815469296, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241035285174219432, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241022939957621542, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241035285174219433, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241022939957621542, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=989, endPage=993, ext={EN=ArticleExt(id=1241022942776193845, articleId=1241022941144609579, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Association analysis between insulin resistance metabolic score and non-alcoholic fatty liver disease, columnId=1228016567443718970, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods Advances, runingTitle=null, highlight=null, articleAbstract=
Objective

Toevaluate the association between insulin resistance and non-alcoholic fatty liver disease (NAFLD) using the insulin resistance metabolic score (METS-IR).

Methods

The study included 1 315 participants from the National Health and Nutrition Examination Survey (NHANES) database from 2017 to 2018, including 740 people with NAFLD and 575 people without NAFLD. Multivariate logistic regression analysis was used to assess the association between METS-IR and the risk of developing NAFLD. Restricted cubic spline regression analysis was performed to examine dose-response relationship after adjustment. Finally, receiver operatingcharacteristic curve analysis was conducted to evaluate the diagnostic performance of METS-IR for NAFLD.

Results

The multivariate logistic regression analysis showed that each unit increase in METS-IR was associated with a 7% higher risk of developing NAFLD (OR=1.07; 95% CI:1.02-1.11). When categorizing METS-IR into quartiles, individuals in the highest quartile had a 3.35 times increased risk compared to those in the lowest quartile (OR=4.35;95% CI:1.38-13.75), showing a significant trend (P-trend <0.05). When the cutoff value is 39.767, the area under the ROC curve for METS-IR in diagnosing NAFLD was 0.788 (SE=0.0126; 95% CI:0.764-0.809).

Conclusion

There is a positive correlation between METS-IR and risk of developing NAFLD, suggesting that it could be used as a potential indicator in the diagnosis of NAFLD.

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目的

采用胰岛素抵抗代谢评分（METS-IR）评估胰岛素抵抗与非酒精性脂肪性肝病（NAFLD）的关联性。

方法

基于2017至2018年美国国家营养与健康调查数据库（NHANES）中1 315名参与者为研究对象，其中包含740例NAFLD患者和575例非NAFLD患者。采用多因素logistic回归分析METS-IR与发生NAFLD风险之间的关联性，使用RCS回归分析剂量反应关系，最后使用ROC曲线评估METS-IR对NAFLD的诊断性能。

结果

多因素logistic回归分析显示，将METS-IR指数作为连续性变量进行分析，METS-IR每上升一个单位，发生NAFLD的风险增高7%（OR=1.07; 95% CI:1.02~1.11）。将METS-IR指数作为分类变量按四分位数进行计算，METS-IR最高组发生NAFLD的风险是最低组的3.35倍（OR=4.35; 95% CI:1.38~13.75），且具有逐渐增高的趋势（P<0.05）。当截断值为39.767时，ROC曲线下面积AUC为0.788（SE=0.0126; 95% CI:0.764~0.809）。

结论

METS-IR与NALFD的患病风险呈正相关，可作为诊断NAFLD的一个潜在指标。

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熊哲锟，E-mail:
, copyrightStatement=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=l52GvRzUg4YNjk/gXbz0MQ==, magXml=gjhHJxbVrgNz/6iM0Kukqg==, pdfUrl=null, pdf=YJfgGEsJEUbyl16MNBx9kA==, pdfFileSize=696163, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=TB5VnbyzM0F0023cc1Qilw==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=ukORXAyaOJUfcetNO2q6Xw==, mapNumber=null, authorCompany=null, fund=null, authors=

邓桃英（1998—），女，硕士在读，研究方向:肝病研究

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邓桃英（1998—），女，硕士在读，研究方向:肝病研究

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General baseline characteristics of participants

, figureFileSmall=null, figureFileBig=null, tableContent=
变量总数（n=1 315)非NAFLD组(n=575)NAFLD组(n=740)统计量值P值
年龄[岁，M(P25,P75)]44(31, 59)38(28, 56)47(34, 60)6.169<0.001
性别[n(%)]3.2670.071
男697(53.0)321(55.8)376(50.8)
女618(47.0)254(44.2)364(49.2)
种族[n(%)]34.491<0.001
墨西哥裔美国人187(14.2)55(9.6)132(17.8)
非西班牙裔黑人340(25.9)186(32.3)154(20.8)
非西班牙裔白人420(31.9)169(29.4)251(33.9)
其他种族368(28.0)165(28.7)203(27.4)
受教育程度[n(%)]2.8720.238
大学及以上770(58.6)347(60.3)423(57.2)
高中或同等教育313(23.8)138(24.0)175(23.6)
高中以下232(17.6)90(15.7)142(19.2)
吸烟[n(%)]0.2220.637
无吸烟史807(61.4)357(62.1)450(60.8)
有吸烟史508(38.6)218(37.9)290(39.2)
饮酒[M(P25, P75)]2.00(1.00, 3.00)2.00(1.00, 3.00)2.00(1.00, 3.00)1.5100.131
体力活动[n(%)]29.474<0.001
久坐634(48.2)241(41.9)393(53.1)
轻度183(13.9)69(12.0)114(15.4)
中度128(9.7)70(12.2)58(7.8)
高度370(28.1)195(33.9)175(23.6)
高血压[n(%)]45.769<0.001
无高血压989(75.2)485(84.3)504(68.1)
高血压326(24.8)90(15.7)236(31.9)
高胆固醇血症[n(%)]3.7860.052
无高胆固醇血症1 052(80.0)474(82.4)578(78.1)
高胆固醇血症263(20.0)101(17.6)162(21.9)
糖尿病[n(%)]16.378<0.001
无糖尿病1 252(95.2)563(97.9)689(93.1)
糖尿病63(4.8)12(2.1)51(6.9)
PIR[M(P25, P75)]2.18(1.20, 4.08)2.22(1.18, 4.08)2.16(1.22, 4.08)0.3490.727
METS-IR[M(P25, P75)]40(33, 48)34(30, 40)44(38, 52)17.908<0.001
METS-IR分类[n(%)]324.410<0.001
Q1329(25.0)254(44.2)75(10.1)
Q2329(25.0)185(32.2)144(19.5)
Q3328(24.9)90(15.7)238(32.2)
Q4329(25.0)46(8.0)283(38.2)
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研究对象的基本情况

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变量总数（n=1 315)非NAFLD组(n=575)NAFLD组(n=740)统计量值P值
年龄[岁，M(P25,P75)]44(31, 59)38(28, 56)47(34, 60)6.169<0.001
性别[n(%)]3.2670.071
男697(53.0)321(55.8)376(50.8)
女618(47.0)254(44.2)364(49.2)
种族[n(%)]34.491<0.001
墨西哥裔美国人187(14.2)55(9.6)132(17.8)
非西班牙裔黑人340(25.9)186(32.3)154(20.8)
非西班牙裔白人420(31.9)169(29.4)251(33.9)
其他种族368(28.0)165(28.7)203(27.4)
受教育程度[n(%)]2.8720.238
大学及以上770(58.6)347(60.3)423(57.2)
高中或同等教育313(23.8)138(24.0)175(23.6)
高中以下232(17.6)90(15.7)142(19.2)
吸烟[n(%)]0.2220.637
无吸烟史807(61.4)357(62.1)450(60.8)
有吸烟史508(38.6)218(37.9)290(39.2)
饮酒[M(P25, P75)]2.00(1.00, 3.00)2.00(1.00, 3.00)2.00(1.00, 3.00)1.5100.131
体力活动[n(%)]29.474<0.001
久坐634(48.2)241(41.9)393(53.1)
轻度183(13.9)69(12.0)114(15.4)
中度128(9.7)70(12.2)58(7.8)
高度370(28.1)195(33.9)175(23.6)
高血压[n(%)]45.769<0.001
无高血压989(75.2)485(84.3)504(68.1)
高血压326(24.8)90(15.7)236(31.9)
高胆固醇血症[n(%)]3.7860.052
无高胆固醇血症1 052(80.0)474(82.4)578(78.1)
高胆固醇血症263(20.0)101(17.6)162(21.9)
糖尿病[n(%)]16.378<0.001
无糖尿病1 252(95.2)563(97.9)689(93.1)
糖尿病63(4.8)12(2.1)51(6.9)
PIR[M(P25, P75)]2.18(1.20, 4.08)2.22(1.18, 4.08)2.16(1.22, 4.08)0.3490.727
METS-IR[M(P25, P75)]40(33, 48)34(30, 40)44(38, 52)17.908<0.001
METS-IR分类[n(%)]324.410<0.001
Q1329(25.0)254(44.2)75(10.1)
Q2329(25.0)185(32.2)144(19.5)
Q3328(24.9)90(15.7)238(32.2)
Q4329(25.0)46(8.0)283(38.2)
), ArticleFig(id=1241022952817357138, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241022941144609579, language=EN, label=Table 2, caption=

Correlation analysis between METS-IR and NAFLD

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变量模型1模型2模型3
OR(95% CI)P值OR(95% CI)P值OR(95% CI)P值
METS-IR（四分位数）
Q11.00(Reference)1.00(Reference)1.00(Reference)
Q22.64(1.88~3.69)<0.0011.44(0.90~2.31)0.1251.41(0.88~2.26)0.152
Q38.96(6.29~12.76)<0.0013.24(1.59~6.58)0.0013.25(1.60~6.60)0.001
Q420.84(13.91~31.22)<0.0014.39(1.38~13.97)0.0124.35(1.38~13.75)0.012
P趋势<0.001<0.001<0.001
METS-IR1.13(1.11~1.15)<0.0011.07(1.02~1.12)<0.0011.07(1.02~1.11)0.004
), ArticleFig(id=1241022952892854617, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241022941144609579, language=CN, label=表2, caption=

METS-IR与NAFLD的关联性分析

, figureFileSmall=null, figureFileBig=null, tableContent=
变量模型1模型2模型3
OR(95% CI)P值OR(95% CI)P值OR(95% CI)P值
METS-IR（四分位数）
Q11.00(Reference)1.00(Reference)1.00(Reference)
Q22.64(1.88~3.69)<0.0011.44(0.90~2.31)0.1251.41(0.88~2.26)0.152
Q38.96(6.29~12.76)<0.0013.24(1.59~6.58)0.0013.25(1.60~6.60)0.001
Q420.84(13.91~31.22)<0.0014.39(1.38~13.97)0.0124.35(1.38~13.75)0.012
P趋势<0.001<0.001<0.001
METS-IR1.13(1.11~1.15)<0.0011.07(1.02~1.12)<0.0011.07(1.02~1.11)0.004
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胰岛素抵抗代谢评分与非酒精性脂肪性肝病的关联性分析
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邓桃英 , 王彩鸾 , 林艳华 , 熊哲锟
现代预防医学 | 流行病与统计方法 2025,52(6): 989-993
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现代预防医学 | 流行病与统计方法 2025, 52(6): 989-993
胰岛素抵抗代谢评分与非酒精性脂肪性肝病的关联性分析
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邓桃英, 王彩鸾, 林艳华, 熊哲锟
作者信息
  • 广州中医药大学附属中山中医院肝病科，广东　中山　528401

    • 邓桃英（1998—），女，硕士在读，研究方向:肝病研究

通讯作者:

熊哲锟，E-mail:
Association analysis between insulin resistance metabolic score and non-alcoholic fatty liver disease
Tao-ying DENG, Cai-luan WANG, Yan-hua LIN, Zhe-kun XIONG
Affiliations
  • Department of Hepatology, Zhongshan Hospital of Traditional Chinese Medicine, Guangzhou University of Chinese Medicine, Zhongshan, Guangdong 528401, China
出版时间: 2025-03-25 doi: 10.20043/j.cnki.MPM.202409473
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摘要
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目的

采用胰岛素抵抗代谢评分（METS-IR）评估胰岛素抵抗与非酒精性脂肪性肝病（NAFLD）的关联性。

方法

基于2017至2018年美国国家营养与健康调查数据库（NHANES）中1 315名参与者为研究对象，其中包含740例NAFLD患者和575例非NAFLD患者。采用多因素logistic回归分析METS-IR与发生NAFLD风险之间的关联性，使用RCS回归分析剂量反应关系，最后使用ROC曲线评估METS-IR对NAFLD的诊断性能。

结果

多因素logistic回归分析显示，将METS-IR指数作为连续性变量进行分析，METS-IR每上升一个单位，发生NAFLD的风险增高7%（OR=1.07; 95% CI:1.02~1.11）。将METS-IR指数作为分类变量按四分位数进行计算，METS-IR最高组发生NAFLD的风险是最低组的3.35倍（OR=4.35; 95% CI:1.38~13.75），且具有逐渐增高的趋势（P<0.05）。当截断值为39.767时，ROC曲线下面积AUC为0.788（SE=0.0126; 95% CI:0.764~0.809）。

结论

METS-IR与NALFD的患病风险呈正相关，可作为诊断NAFLD的一个潜在指标。

非酒精性脂肪性肝病  /  胰岛素抵抗代谢评分  /  NHANES数据库
Objective

Toevaluate the association between insulin resistance and non-alcoholic fatty liver disease (NAFLD) using the insulin resistance metabolic score (METS-IR).

Methods

The study included 1 315 participants from the National Health and Nutrition Examination Survey (NHANES) database from 2017 to 2018, including 740 people with NAFLD and 575 people without NAFLD. Multivariate logistic regression analysis was used to assess the association between METS-IR and the risk of developing NAFLD. Restricted cubic spline regression analysis was performed to examine dose-response relationship after adjustment. Finally, receiver operatingcharacteristic curve analysis was conducted to evaluate the diagnostic performance of METS-IR for NAFLD.

Results

The multivariate logistic regression analysis showed that each unit increase in METS-IR was associated with a 7% higher risk of developing NAFLD (OR=1.07; 95% CI:1.02-1.11). When categorizing METS-IR into quartiles, individuals in the highest quartile had a 3.35 times increased risk compared to those in the lowest quartile (OR=4.35;95% CI:1.38-13.75), showing a significant trend (P-trend <0.05). When the cutoff value is 39.767, the area under the ROC curve for METS-IR in diagnosing NAFLD was 0.788 (SE=0.0126; 95% CI:0.764-0.809).

Conclusion

There is a positive correlation between METS-IR and risk of developing NAFLD, suggesting that it could be used as a potential indicator in the diagnosis of NAFLD.

Non-alcoholic fatty liver disease  /  Insulin Resistance Metabolic Score  /  NHANES database
邓桃英, 王彩鸾, 林艳华, 熊哲锟. 胰岛素抵抗代谢评分与非酒精性脂肪性肝病的关联性分析. 现代预防医学, 2025 , 52 (6) : 989 -993 . DOI: 10.20043/j.cnki.MPM.202409473
Tao-ying DENG, Cai-luan WANG, Yan-hua LIN, Zhe-kun XIONG. Association analysis between insulin resistance metabolic score and non-alcoholic fatty liver disease[J]. Modern Preventive Medicine, 2025 , 52 (6) : 989 -993 . DOI: 10.20043/j.cnki.MPM.202409473
正文
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随着生活方式的改变，非酒精性脂肪性肝病（Non-alcoholic fatty liver disease, NAFLD）成为全球范围内最常见的肝病之一，且是导致慢性肝病如肝硬化和肝细胞癌的主要原因[1]。大多数早期NAFLD患者缺乏明显的临床表现，在进展为晚期阶段如肝硬化或肝癌时才被确诊[2]。这对全球肝脏健康和代谢领域构成了新挑战。因此，早期识别和预防NAFLD具有重要意义。
胰岛素抵抗（Insulin resistance, IR）是NAFLD发病的重要原因[3]，其通过增加游离脂肪酸输送和高胰岛素血症刺激合成代谢过程来增强肝脏脂肪积累，从而引起肝脏脂肪变性诱导氧化应激反应的发生[4]。在临床实践中，根据欧洲和亚洲的相关指南，IR的评估已成为NAFLD患者诊断和预后分层的重要环节[5-6]。但是，目前IR的评估方法存在诸多缺陷，例如高胰岛素正葡萄糖钳夹（Hyperinsulinemic Euglycemic Clamp, HEC）作为公认的金标准[7]，由于其复杂且侵入性的特点以及高昂成本的限制，使得其在临床实践中应用受限。目前，胰岛素稳态模型评估（Homeostasis model assessment of insulin resistance, HOMA-IR）因其简单操作和接近无创的优势已广泛取代HEC并被广泛应用于临床实践。然而，在接受胰岛素治疗或β细胞功能失调患者中，该模型预测准确性有限[8]。胰岛素抵抗代谢评分（Metabolic score for insulin resistance, METS-IR）作为一种简单、间接的IR评估方法，其主要由身体测量数据及基本代谢指标组成，这不仅避免了昂贵且可变的空腹胰岛素测试的需要，同时其测算指标也与肥胖、血脂异常、高血糖和腹内脂肪堆积等NAFLD的潜在危险因素相对应，已成为了评估NAFLD发生风险的有前景的指标[7,9]，目前研究多针对中老年人群开展[10-11]，但据相关研究显示，中年人群NAFLD的发病率最高[12]，导致其临床适用性不足且研究结果对早期预防意义不高。鉴于此，本研究利用美国国家健康和营养检查调查(National Health and Nutrition Examination Survey, NHANES)的数据，探讨成年人群中METS-IR与NAFLD的关联，旨在为NAFLD的风险评估提供更为全面的证据支持，推动该领域研究的进展。
1　材料和方法
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1.1　研究人群
本研究使用的数据来源于美国国家卫生统计中心设计和开展的全国代表性横断面调查，该调查利用分层、多阶段概率抽样方法，系统地收集了具有全国代表性美国平民人口的健康和营养统计数据[13]。为了评估参与者的营养和身体健康状况，在流动检查中心进行了标准化的家庭访谈、体检和实验室测试。调查已获得伦理委员会的批准并获得了所有参与者的知情同意书。2017年至2018年期间NHANES数据库有9 254名参与者，本研究排除了3 685名年龄小于20岁、753名缺少NAFLD完整数据以及3 501名缺少METS-IR相关数据的参与者，最终有1 315名参与者被纳入研究。
1.2　METS-IR指数的测量
在本研究中，将METS-IR设置为暴露变量。METS-IR=Ln[(2×空腹血糖(mg/dl))+空腹甘油三酯(mg/dl)]×体重指数(kg/m2)/[Ln(高密度脂蛋白胆固醇(mg/dl))] [14]。使用自动生化分析仪酶法测量夜间禁食8.5小时后早晨收集血样的空腹血糖和甘油三酯及肝功能。
1.3　NAFLD的评估
NAFLD的定义是指在排除大量饮酒个体（女性饮酒>20 g/d或男性饮酒>30 g/d）和其他引起肝脏脂肪变性病因（如乙、丙、丁型肝炎）后，超声检查中存在肝脏脂肪变性。本研究使用具有受控衰减参数（the controlled attenuation parameter, CAP）的肝脏瞬时弹性测定来评估是否存在肝脏脂肪变性[15]，研究人群中CAP评分≥238 dB/m者定义为NAFLD[16]。
1.4　协变量
人口统计学变量包括年龄、性别、人种、受教育程度、家庭贫困收入比（the family poverty income ratio, PIR）、吸烟状况、酒精摄入量、体力活动。健康风险因素包括糖尿病、高血压和高胆固醇血症。所有这些协变量都可以从NHANES网站上的各种数据模块中获得。
种族背景分为四组：墨西哥裔美国人、非西班牙裔白人、非西班牙裔黑人和其他种族。受教育程度分为三个层次：9年以下（高中以下）、9至12年（高中或同等教育）、12年以上（大学或以上）。家庭收入根据美国政府报告中定义的贫困收入比（PIR）进行描述。吸烟定义为一个人一生中至少吸过100支香烟。饮酒状况为过去12个月平均每日饮酒量[17]。体力活动水平分为久坐，轻度，中度或剧烈。参与者根据以下任何一项被归类为糖尿病：医生或健康专业人员诊断为糖尿病；正在使用胰岛素或糖尿病药物；空腹血糖浓度≥7.0 mmol/L；2小时口服葡萄糖耐量试验结果≥11.1 mmol/L；或糖化血红蛋白HbA1c水平≥6.5%[18]。患有高血压定义为已被医生诊断患有高血压，或正在服用高血压药物，或收缩压水平≥140 mm Hg，或舒张压水平≥90 mm Hg。高胆固醇血症定义为被医生告知其胆固醇水平较高或正在服用治疗高胆固醇血症药物，或总胆固醇值≥240 mg/dl。
1.5　统计学分析
本研究采用R 4.2.3进行统计分析。对于正态分布数据，连续变量以表示；对于非正态分布数据，则以M(P25,P75)表示。组间比较使用t检验或Wilcoxon检验进行连续数据的比较。分类数据则用数字和百分比表示，并采用卡方检验进行分析。应用多因素logistic回归模型来探究METS-IR与NAFLD之间的相关性，其中METS-IR作为连续变量，建立三个多因素logistic回归模型中计算NAFLD风险的比值比（OR）和95% 置信区间（CI）。在模型1中未调整任何变量，在模型2中调整了年龄、性别、人种、受教育程度、PIR、吸烟状况和饮酒情况等基本协变量；在模型3中进一步调整了高血压疾病史。通过限制性立方样条分析（RCS），在METS-IR的5%、35%、65%和95%处分析并展示其与NAFLD潜在关联及剂量-反应关系。利用ROC曲线和AUC评估METS-IR对NAFLD的诊断能力。所有检验均采用双侧检验，显著性水平α设定为0.05。
2　结果
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2.1　参与者的基线特征
共有1 315名研究对象纳入分析，其中NAFLD患者740人，非NAFLD 575人。结果显示：非NAFLD组患者的中位年龄为38岁，NAFLD组患者的中位年龄为47岁（P<0.001），非NAFLD组的非西班牙裔黑人比例（32.3%）高于NAFLD组（20.8%），而NAFLD组墨西哥裔美国人比例（17.8%）高于非NAFLD组（9.6%），高血压、高胆固醇血症、糖尿病和体力活动方面也存在明显差异（均P<0.001）。患有NAFLD组的METS-IR水平显著高于非NAFLD组，以上特征两组的分布差异均有统计学意义（P<0.05或0.01），见表1。
2.2　METS-IR与NAFLD的多因素logistic分析
将METS-IR作为连续性变量进行分析，调整了年龄、性别、人种、受教育程度、PIR、吸烟、饮酒、体力活动、高血压等协变量后可以发现METS-IR每上升一个单位，发生NAFLD的风险增高7%（OR=1.07; 95% CI:1.02~1.11）。将METS-IR从连续变量作为分类变量按四分位数进行计算，与METS-IR指数最低组（Q1）相比，METS-IR最高组（Q4）新发生NAFLD的风险增加至3.35倍（OR=4.35; 95% CI:1.38~13.75），并且具有增高的趋势（P<0.05），见表2。
2.3　METS-IR与NAFLD的剂量-反应关系
METS-IR与NAFLD发生风险的剂量-反应关系如图1所示。调整了年龄、性别、人种、教育程度、PIR、吸烟、饮酒、体力活动、高血压等协变量后进一步使用限制性三次样条回归分析，结果显示METS-IR与NAFLD发病率之间存在剂量反应关系（P<0.001）且为线性关系（PNonliner=0.009），当METS-IR大于39.782时，METS-IR为NAFLD的危险因素，且累积发病风险逐渐增加。
2.4　METS-IR与NAFLD的ROC曲线分析
基于RCS的结果，计算ROC曲线下面积来评估METS-IR对NAFLD的诊断性能。结果显示，ROC曲线下面积（AUC）为0.788（SE=0.0126; 95% CI:0.764~0.809）。通过进一步分析，选取约登指数最大值对应的截断值，计算出METS-IR的最佳截断值为39.767，与RCS的结果类似，表明计算出的结果具有稳定性，如图2。
3　讨论
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迄今为止，已有大量文献表明NAFLD和IR密切相关，并有研究证明，IR是NAFLD发生和发展的强有力的驱动力[4-6]。尽管目前尚未明确IR与肝脏新合成脂肪（de novo lipogenesis, DNL）关联的确切机制，但可以从几个潜在的生物途径进行解释。首先，胰岛素信号传导异常在IR状态下，PI3K/Akt通路的功能受损，引起循环中胰岛素和血糖浓度增加可能是刺激DNL产生的原因之一，在这过程中胰岛素和血糖分别通过活化固醇调节元件结合蛋白1c（SREBP-1c）和碳水化合物反应元件结合蛋白，来激活参与DNL转录过程中所涉及基因[19-21]。动物实验发现，在存在IR小鼠模型如ob/ob或者由高能量摄入引起的肥胖模型中，仅仅缺失特异于肝脏组织内部源性胰岛素受体（the insulin receptor, INSR）就足以降低SREBP-1c的表达水平，这足以说明SREBP-1c是参与调控肝内DNL过程中至关重要的转录因子之一[22]。其次，葡萄糖和脂质代谢的失调表现为肝脏通过糖酵解和丙酮酸脱氢酶活性增加，将过量葡萄糖转化为脂肪酸，同时脂质合成酶（如脂肪酸合成酶和乙酰辅酶A羧化酶）的上调进一步促进DNL[23-24]。此外，IR常伴随慢性低度炎症和氧化应激，促炎因子如TNF-α和IL-6通过影响胰岛素信号通路，间接促进DNL，而氧化应激则可能通过损伤信号传导途径增加脂质合成[25]。IR还可导致脂肪组织中脂解作用增加，释放更多的非酯化脂肪酸（NEFA）进入肝脏，提供DNL的底物[26]。并且，IR可能抑制肝细胞自噬，导致细胞内脂质积累，进一步促进DNL[27]。这些途径共同作用，可能解释了IR与肝脏新合成脂肪之间的复杂关系。
IR相关的生物标志物对于NALFD的早期检测和并发症风险预测具有一定程度的优势[28]，但目前IR的检测方法存在高成本、胰岛功能高度依赖等缺点，因此基于空腹血糖、甘油三酯、高密度脂蛋白胆固醇和体重指数水平的MEST-IR被开发出来。METS-IR整合了肥胖和代谢综合征的各个方面，避免了对空腹胰岛素水平的过度依赖[7]。相关研究也证实了METS-IR与NALFD之间存在潜在关联[10-11]。但这些研究均基于中老年人群，对人群NAFLD真实患病率的代表性不足。
本研究基于美国代表性的大样本横断面调查，探讨了METS-IR与NAFLD之间的关联。结果表明，METS-IR与NAFLD的患病率显著相关，且METS-IR与NAFLD之间存在剂量反应关系。当METS-IR阈值为39.782时，ROC曲线下面积（AUC）为0.788，这表明将METS-IR纳入NAFLD评估中有助于为临床医生提供准确可行的早期预测标准，这种整合为个性化和更有效的预防策略奠定了基础。
本研究也存在一些局限性。首先，由于该研究为横断面设计，无法确定METS-IR与NAFLD之间的时间关联。其次，并未消除其他潜在混杂因素（如饮食模式、遗传倾向和社会心理压力等）带来的偏倚。第三，横断面研究存在依赖数据库中诊断来直接替代临床测量或医疗机构诊断的固有特性，可能引入潜在偏差。最后，由于本研究是基于美国NHANES数据库的分析，其结果对中国人群的适用性仍有待考量。
综上，METS-IR与NAFLD患病率存在正向相关，且有剂量效应关系，METS-IR可能成为一项评估NAFLD风险的有效工具，两者的因果关系需要进一步的纵向研究来验证。
基金
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  • 国家中医药传承创新项目(YN2024A003; YN2024B014)
  • 市级名中医药专家传承工作室项目(中府函[2023]95号)
文献
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doi: 10.20043/j.cnki.MPM.202409473
  • 接收时间：2024-09-26
  • 首发时间：2026-03-18
  • 出版时间：2025-03-25
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  • 收稿日期：2024-09-26
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国家中医药传承创新项目(YN2024A003; YN2024B014)
市级名中医药专家传承工作室项目(中府函[2023]95号)
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    广州中医药大学附属中山中医院肝病科，广东　中山　528401

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熊哲锟，E-mail:
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2种不同金属材料的力学参数
科
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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