Article(id=1311350310870806833, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1311350193946194882, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202601444, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1769270400000, receivedDateStr=2026-01-25, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790579877821, onlineDateStr=2026-09-28, pubDate=1786291200000, pubDateStr=2026-08-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790579877821, onlineIssueDateStr=2026-09-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790579877821, creator=13701087609, updateTime=1790579877821, updator=13701087609, issue=Issue{id=1311350193946194882, tenantId=1146029695717560320, journalId=1227665162245664772, year='2026', volume='53', issue='15', pageStart='2689', pageEnd='2880', issueExtLink='null', onlineDate='null', pubDate='1786291200000', pubDateStr='2026-08-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790579849945, creator='13701087609', updateTime=1790588319569, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1311385718195315029, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1311350193946194882, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1311385718195315030, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1311350193946194882, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=2699, endPage=2705, ext={EN=ArticleExt(id=1311350311055356210, articleId=1311350310870806833, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Association of insulin resistance surrogate indices, lipid metabolism, and obesity-related indicators with hyperuricemia in middle-aged and elderly adults in Guangxi, China, columnId=1240413921954295836, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the associations of nine indices related to insulin resistance, lipid metabolism, and obesity,including estimated glucose disposal rate (eGDR), triglyceride glucose index (TyG), metabolic score for insulin resistance(METS-IR), triglyceride to high-density lipoprotein cholesterol ratio (TG/HDL-C ratio), TyG-waist circumference (TyG-WC),TyG-body mass index (TyG-BMI), TyG-waist-to-height ratio (TyG-WHtR), a body shape index (ABSI), and conicity index(CI), with hyperuricemia (HUA) among middle-aged and elderly individuals in a county in Guangxi, to screen for key indicators, and to validate them through a cohort study.

Methods

A combined cross-sectional and prospective cohort study design was employed. Multivariable logistic regression analysis was used to evaluate the association between each index and HUA. Five machine learning models, including XGBoost, were constructed to evaluate the performance of these indices and screen for key indices. In the prospective cohort, modified Poisson regression and restricted cubic splines (RCS) were utilized to analyze the association and dose-response relationship between the most critical index and the risk of incident HUA.

Results

Multivariable logistic regression showed that after adjusting for covariates, TyG, METS-IR, TG/HDL-C ratio, TyG-WC, TyG-BMI, TyG-WHtR, ABSI, and CI were positively associated with HUA, whereas eGDR was negatively associated with HUA (P<0.001). The machine learning model results indicated that the TG/HDL-C ratio was the most critical index for identifying HUA.A non-linear association was observed between the TG/HDL-C ratio and the risk of incident HUA (P<0.001), with a threshold of 2.971 for the TG/HDL-C ratio in relation to HUA.

Conclusion

All nine indices were associated with HUA in middle-aged and elderly people. The TG/HDL-C ratio was the most critical index for identifying HUA, which holds important public health significance for the early identification of individuals with hyperuricemia.

, authors=Mo-qi ZHANG, Yuan YANG, Chao-fan XIE, Ze-yan YE, Zhi-yong ZHANG, authorsList=Mo-qi ZHANG, Yuan YANG, Chao-fan XIE, Ze-yan YE, Zhi-yong ZHANG, authorCompany=null, correspAuthors=Zhi-yong 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=1311350315169968462, articleId=1311350310870806833, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=广西中老年人胰岛素抵抗替代指数、脂质代谢及肥胖相关指数与高尿酸血症的关联性研究, columnId=1228016567632462653, journalTitle=现代预防医学, columnName=流行病与统计方法, runingTitle=null, highlight=null, articleAbstract=
目的

探究估计葡萄糖处理率 (estimated glucose disposal rate,eGDR)、甘油三酯-葡萄糖指数(triglyceride glucose index,TyG)、胰岛素抵抗代谢得分(metabolic score for insulin resistance,METS-IR)、甘油三酯与高密度脂蛋白胆固醇比值(triglyceride to high-density lipoprotein cholesterol ratio,TG/HDL-C Ratio)、TyG-腰围(TyG-waist circumference,TyG-WC)、TyG-身体质量指数(TyG-body mass index,TyG-BMl)、TyG-腰高比(TyG-waist-to-height ratio,TyG-WHtR)、身体形态指数(a body shape index,ABSI)及圆锥指数等9种胰岛素抵抗、脂质代谢及肥胖相关指数与广西某县中老年人高尿酸血症(hyperuricemia,HUA)的关联,筛选关键指标并通过前瞻性队列验证。

方法

采用横断面与前瞻性队列相结合的研究设计。通过多因素logistic回归分析各指标与HUA的关联;构建XGBoost等五种机器学习模型评估指数的效能并筛选关键指数。前瞻性队列中,采用修正泊松回归和限制性立方样条分析最关键指数与HUA发病风险的关联及剂量-反应关系。

结果

多因素logistic回归显示,调整协变量后,TyG、METS-IR、TG/HDL-C Ratio、TyG-WC、TyG-BMI、TyG-WHtR, ABSI和圆锥指数与HUA呈正相关,eGDR与HUA呈负相关(P<0.001)。机器学习模型结果发现,TG/HDL-C Ratio为识别HUA的最关键指数。TG/HDL-C Ratio与HUA发病风险呈非线性关联(P<0.001),TG/HDL-C Ratio与HUA的阈值为2.971。

结论

九种指标均与中老年人HUA相关,TG/HDL-C Ratio是识别HUA的最关键指数,对HUA人群的早期识别具有重要公共卫生意义。

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张墨奇(1999——),女,硕士在读,研究方向:环境与健康

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张志勇,E-mail:
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注:模型1未调整模型;模型2调整了性别、年龄、民族、受教育程度、吸烟、饮酒、高血压及糖尿病;CI为圆锥指数。

, figureFileSmall=Lt6IpjMJuEhFtCAD/rnRBA==, figureFileBig=5zeIbnifwr08yC84fxXQfg==, tableContent=null), ArticleFig(id=1311350319200694647, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350310870806833, language=EN, label=Figure 2, caption= Composite figure of model screening and key indicator identification, figureFileSmall=RUyXCmzM31YF0GZyu99+0w==, figureFileBig=VD7MzwcuByXM7RM52gTOXA==, tableContent=null), ArticleFig(id=1311350319288775032, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350310870806833, language=CN, label=图2, caption= 多模型筛选与关键指标识别图

注:图A为基于机器学习的HUA各模型测试集ROC曲线;图B为各模型测试集AUC值排序图;图C为SHAP重要性排序图;图D为SHAP 蜂群图;CI为圆锥指数。

, figureFileSmall=RUyXCmzM31YF0GZyu99+0w==, figureFileBig=VD7MzwcuByXM7RM52gTOXA==, tableContent=null), ArticleFig(id=1311350319364272505, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350310870806833, language=EN, label=Figure 3, caption=Forest plot showing the association between TG/HDL-C ratio and hyperuricemia based on modified Poisson regression, figureFileSmall=fSRci08pD+fs8q3WkL4ptQ==, figureFileBig=Rov8U8Jb9/lyYEWIxKGt5Q==, tableContent=null), ArticleFig(id=1311350319460741498, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350310870806833, language=CN, label=图3, caption=TG/HDL-C Ratio与HUA的关联修正泊松回归结果森林图

注:模型1未调整模型;模型2调整了性别、年龄、民族、受教育程度、吸烟、饮酒、高血压及糖尿病。

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注:此结果调整了性别、年龄、民族、受教育程度、吸烟、饮酒、高血压及糖尿病。

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General demographic characteristics of the study population [M (P25, P75), n(%)]

, figureFileSmall=null, figureFileBig=null, tableContent=
特征总人群(n=3 071)非HUA(n=2 724)HUA(n=347)t/χ2值P值
年龄(岁)62 (53, 68)62 (53, 68)63 (54, 69)-1.210.225
性别257.05<0.001
女1 866 (60.76)1 793 (65.82)73 (21.04)
男1 205 (39.24)931 (34.18)274 (78.96)
民族0.2550.880
汉族685 (22.31)604 (22.17)81 (23.34)
瑶族2 246 (73.14)1 996 (73.27)250 (72.05)
其他140 (4.55)124 (4.55)16 (4.61)
吸烟79.691<0.001
否2 467 (80.33)2 251 (82.64)216 (62.25)
是604 (19.67)473 (17.36)131 (37.75)
饮酒39.226<0.001
否2 055 (66.92)1 875 (68.83)180 (51.87)
是1 016 (33.08)849 (31.17)167 (48.13)
糖尿病6.9490.008
否2 910 (94.76)2 592 (95.15)318 (91.64)
是161 (5.24)132 (4.85)29 (8.36)
高血压28.396<0.001
否2 169 (70.63)1 967 (72.21)202 (58.21)
是902 (29.37)757 (27.79)145 (41.79)
受教育程度43.360<0.001
未接受正规教育535 (17.42)500 (18.36)35 (10.09)
小学1 565 (50.96)1 414 (51.91)151 (43.52)
初中及以上971 (31.62)810 (29.74)161 (46.40)
TyG8.33 (7.96, 8.79)8.30 (7.94, 8.73)8.72 (8.23, 9.21)-9.797<0.001
METS-IR29.17 (26.14, 32.93)28.87 (25.95, 32.48)31.77 (28.35, 35.63)-8.866<0.001
TG/HDL-C Ratio0.63 (0.40, 1.04)28.87 (25.95, 32.48)31.77 (28.35, 35.63)-9.436<0.001
eGDR10.49 (8.12, 11.37)10.60 (8.31, 11.43)9.24 (7.16, 10.56)9.163<0.001
TyG-BMI185.64 (164.80, 214.12)182.66 (163.19, 209.39)210.18 (182.19, 236.24)-9.945<0.001
TyG-WC652.80 (582.25, 736.33)645.81 (577.23, 720.87)737.71 (648.70, 818.04)-12.145<0.001
TyG-WHtR4.23 (3.77, 4.78)4.19 (3.74, 4.72)4.64 (4.09, 5.17)-8.955<0.001
ABSI0.79 (0.76, 0.83)0.79 (0.76, 0.83)0.80 (0.77, 0.83)-3.0700.002
圆锥指数1.23 (1.16, 1.29)1.22 (1.16, 1.29)1.25 (1.19, 1.31)-5.559<0.001
), ArticleFig(id=1311350319813063038, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350310870806833, language=CN, label=表1, caption=

社会人口学特征[M(P25,P75),n(%)]

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特征总人群(n=3 071)非HUA(n=2 724)HUA(n=347)t/χ2值P值
年龄(岁)62 (53, 68)62 (53, 68)63 (54, 69)-1.210.225
性别257.05<0.001
女1 866 (60.76)1 793 (65.82)73 (21.04)
男1 205 (39.24)931 (34.18)274 (78.96)
民族0.2550.880
汉族685 (22.31)604 (22.17)81 (23.34)
瑶族2 246 (73.14)1 996 (73.27)250 (72.05)
其他140 (4.55)124 (4.55)16 (4.61)
吸烟79.691<0.001
否2 467 (80.33)2 251 (82.64)216 (62.25)
是604 (19.67)473 (17.36)131 (37.75)
饮酒39.226<0.001
否2 055 (66.92)1 875 (68.83)180 (51.87)
是1 016 (33.08)849 (31.17)167 (48.13)
糖尿病6.9490.008
否2 910 (94.76)2 592 (95.15)318 (91.64)
是161 (5.24)132 (4.85)29 (8.36)
高血压28.396<0.001
否2 169 (70.63)1 967 (72.21)202 (58.21)
是902 (29.37)757 (27.79)145 (41.79)
受教育程度43.360<0.001
未接受正规教育535 (17.42)500 (18.36)35 (10.09)
小学1 565 (50.96)1 414 (51.91)151 (43.52)
初中及以上971 (31.62)810 (29.74)161 (46.40)
TyG8.33 (7.96, 8.79)8.30 (7.94, 8.73)8.72 (8.23, 9.21)-9.797<0.001
METS-IR29.17 (26.14, 32.93)28.87 (25.95, 32.48)31.77 (28.35, 35.63)-8.866<0.001
TG/HDL-C Ratio0.63 (0.40, 1.04)28.87 (25.95, 32.48)31.77 (28.35, 35.63)-9.436<0.001
eGDR10.49 (8.12, 11.37)10.60 (8.31, 11.43)9.24 (7.16, 10.56)9.163<0.001
TyG-BMI185.64 (164.80, 214.12)182.66 (163.19, 209.39)210.18 (182.19, 236.24)-9.945<0.001
TyG-WC652.80 (582.25, 736.33)645.81 (577.23, 720.87)737.71 (648.70, 818.04)-12.145<0.001
TyG-WHtR4.23 (3.77, 4.78)4.19 (3.74, 4.72)4.64 (4.09, 5.17)-8.955<0.001
ABSI0.79 (0.76, 0.83)0.79 (0.76, 0.83)0.80 (0.77, 0.83)-3.0700.002
圆锥指数1.23 (1.16, 1.29)1.22 (1.16, 1.29)1.25 (1.19, 1.31)-5.559<0.001
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广西中老年人胰岛素抵抗替代指数、脂质代谢及肥胖相关指数与高尿酸血症的关联性研究
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张墨奇 , 杨远 , 谢超凡 , 叶泽严 , 张志勇
现代预防医学 | 流行病与统计方法 2026,53(15): 2699-2705
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现代预防医学 |流行病与统计方法 2026 , 53 (15) : 2699 -2705
广西中老年人胰岛素抵抗替代指数、脂质代谢及肥胖相关指数与高尿酸血症的关联性研究
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张墨奇(1999——),女,硕士在读,研究方向:环境与健康

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张墨奇, 杨远, 谢超凡, 叶泽严, 张志勇
作者信息
  • 桂林医科大学公共卫生学院,广西 桂林 541199
通讯作者:
张志勇,E-mail:
作者简介:

张墨奇(1999——),女,硕士在读,研究方向:环境与健康

Association of insulin resistance surrogate indices, lipid metabolism, and obesity-related indicators with hyperuricemia in middle-aged and elderly adults in Guangxi, China
Mo-qi ZHANG, Yuan YANG, Chao-fan XIE, Ze-yan YE, Zhi-yong ZHANG
Affiliations
  • School of Public Health, Guilin Medical University, Guilin, Guangxi 541199, China
出版时间: 2026-08-10 doi: 10.20043/j.cnki.MPM.202601444
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目的

探究估计葡萄糖处理率 (estimated glucose disposal rate,eGDR)、甘油三酯-葡萄糖指数(triglyceride glucose index,TyG)、胰岛素抵抗代谢得分(metabolic score for insulin resistance,METS-IR)、甘油三酯与高密度脂蛋白胆固醇比值(triglyceride to high-density lipoprotein cholesterol ratio,TG/HDL-C Ratio)、TyG-腰围(TyG-waist circumference,TyG-WC)、TyG-身体质量指数(TyG-body mass index,TyG-BMl)、TyG-腰高比(TyG-waist-to-height ratio,TyG-WHtR)、身体形态指数(a body shape index,ABSI)及圆锥指数等9种胰岛素抵抗、脂质代谢及肥胖相关指数与广西某县中老年人高尿酸血症(hyperuricemia,HUA)的关联,筛选关键指标并通过前瞻性队列验证。

方法

采用横断面与前瞻性队列相结合的研究设计。通过多因素logistic回归分析各指标与HUA的关联;构建XGBoost等五种机器学习模型评估指数的效能并筛选关键指数。前瞻性队列中,采用修正泊松回归和限制性立方样条分析最关键指数与HUA发病风险的关联及剂量-反应关系。

结果

多因素logistic回归显示,调整协变量后,TyG、METS-IR、TG/HDL-C Ratio、TyG-WC、TyG-BMI、TyG-WHtR, ABSI和圆锥指数与HUA呈正相关,eGDR与HUA呈负相关(P<0.001)。机器学习模型结果发现,TG/HDL-C Ratio为识别HUA的最关键指数。TG/HDL-C Ratio与HUA发病风险呈非线性关联(P<0.001),TG/HDL-C Ratio与HUA的阈值为2.971。

结论

九种指标均与中老年人HUA相关,TG/HDL-C Ratio是识别HUA的最关键指数,对HUA人群的早期识别具有重要公共卫生意义。

高尿酸血症  /  胰岛素抵抗  /  机器学习  /  前瞻性队列研究
Objective

To explore the associations of nine indices related to insulin resistance, lipid metabolism, and obesity,including estimated glucose disposal rate (eGDR), triglyceride glucose index (TyG), metabolic score for insulin resistance(METS-IR), triglyceride to high-density lipoprotein cholesterol ratio (TG/HDL-C ratio), TyG-waist circumference (TyG-WC),TyG-body mass index (TyG-BMI), TyG-waist-to-height ratio (TyG-WHtR), a body shape index (ABSI), and conicity index(CI), with hyperuricemia (HUA) among middle-aged and elderly individuals in a county in Guangxi, to screen for key indicators, and to validate them through a cohort study.

Methods

A combined cross-sectional and prospective cohort study design was employed. Multivariable logistic regression analysis was used to evaluate the association between each index and HUA. Five machine learning models, including XGBoost, were constructed to evaluate the performance of these indices and screen for key indices. In the prospective cohort, modified Poisson regression and restricted cubic splines (RCS) were utilized to analyze the association and dose-response relationship between the most critical index and the risk of incident HUA.

Results

Multivariable logistic regression showed that after adjusting for covariates, TyG, METS-IR, TG/HDL-C ratio, TyG-WC, TyG-BMI, TyG-WHtR, ABSI, and CI were positively associated with HUA, whereas eGDR was negatively associated with HUA (P<0.001). The machine learning model results indicated that the TG/HDL-C ratio was the most critical index for identifying HUA.A non-linear association was observed between the TG/HDL-C ratio and the risk of incident HUA (P<0.001), with a threshold of 2.971 for the TG/HDL-C ratio in relation to HUA.

Conclusion

All nine indices were associated with HUA in middle-aged and elderly people. The TG/HDL-C ratio was the most critical index for identifying HUA, which holds important public health significance for the early identification of individuals with hyperuricemia.

Hyperuricemia  /  Insulin resistance  /  Machine learning  /  Prospective cohort study
张墨奇, 杨远, 谢超凡, 叶泽严, 张志勇. 广西中老年人胰岛素抵抗替代指数、脂质代谢及肥胖相关指数与高尿酸血症的关联性研究. 现代预防医学, 2026 , 53 (15) : 2699 -2705 . DOI: 10.20043/j.cnki.MPM.202601444
Mo-qi ZHANG, Yuan YANG, Chao-fan XIE, Ze-yan YE, Zhi-yong ZHANG. Association of insulin resistance surrogate indices, lipid metabolism, and obesity-related indicators with hyperuricemia in middle-aged and elderly adults in Guangxi, China[J]. Modern Preventive Medicine, 2026 , 53 (15) : 2699 -2705 . DOI: 10.20043/j.cnki.MPM.202601444
高尿酸血症(hyperuricemia,HUA)是嘌呤代谢紊乱所致的疾病,当血清尿酸> 420 μmol/L时为HUA[1]。2018年,中国HUA患病率11.37%,而至2023年,中国HUA患病率为14.54%[2]。HUA已成为一个重要的公共卫生问题。
胰岛素抵抗(insulin resistance,IR)是机体外源性或内源性胰岛素产生代谢反应受损的一种状态[3]。评估IR的金标准高胰岛素-正常血糖钳6尺寸夹法[4],其使用复杂且成本高,在临床中应用有限。一些基于常规生化测试的胰岛素抵抗替代指标,包括甘油三酯-葡萄糖指数(triglyceride glucose index,TyG)及其与肥胖综合指数TyG-腰围(TyG-waist circumference,TyG-WC)、TyG-身体质量指数(TyG-body mass index,TyG-BMI)、TyG-腰高比(TyG-waist-to-height ratio, TyG-WHtR)、甘油三酯与高密度脂蛋白胆固醇比值(triglyceride to high-density lipoprotein cholesterol ratio, TG/HDL-C Ratio)、胰岛素抵抗代谢评分(metabolic score for insulin resistance,METS-IR)和估计葡萄糖处理率(estimated glucose disposal rate,eGDR)[5-7]。除经典指标BMI外,身体形态指数(a body shape index, ABSI)、圆锥指数[8]能更好反映脂肪分布。已有研究表明TyG、TG/HDL-C Ratio、METS-IR与HUA之间存在显著关联[9],然而,大多数研究采用横断面研究方法,尚缺乏前瞻性证据阐明其因果关联。因此,亟须基于前瞻性队列研究,系统整合IR替代数、脂质代谢和肥胖指数并探究其与HUA的关联,实现HUA的早期预防。
本研究来自广西生态-环境健康中老年队列,调查于2018—2019年广西某县,共收集当地4 356例居民的信息。本研究按照《赫尔辛基宣言》的指导方针进行,涉及人体受试者的所有程序均获得桂林医学院伦理委员会的批准(编号:20180702-3,GYLL2022060)。所有参与者都提供了书面知情同意书。
纳入标准:(1)该县45岁以上常住居民;(2)可提供完整有效的体检数据及面对面访谈信息的调查者。排除标准:(1)患有肝硬化、恶性肿瘤、精神类疾病者;(2)用于计算核心研究指标所需的身高、体重、腰围、空腹血糖等数据缺失者。最后,共有3 071名研究对象纳入分析。
广西生态-环境健康中老年队列在2023—2024年完成首次随访,随访信息通过标准化问卷调查、疾病预防控制中心慢性病监测系统的电子健康档案及随访的体检数据获取。在前瞻性队列分析中,进一步排除基线HUA患者、计算核心研究指数所需的身高、体重、腰围、空腹血糖等数据缺失者及患有肝硬化、恶性肿瘤、精神类疾病者。最终纳入前瞻性队列分析研究共1 583人。
本研究采用9种指数进行计算与分析,包括eGDR、TyG、METS-IR、TG/HDL-C Ratio、TyG-WC、TyG-BMI、TyG-WHtR、ABSI与圆锥指数。各指标计算公式如下:
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
其中,WC为腰围,HTN为高血压(1=是,2=否),HbA1c为糖化血红蛋白,FPG为空腹血糖、TG为甘油三酯,Height为身高。
统一培训的调查员使用校准仪器测量身高、体重、腰围、臀围及血压等;所有受试者签署知情同意书后,采集清晨空腹(禁食≥8 h)静脉血样本。选用日立7600-020全自动生化分析仪检测尿酸、空腹血糖、糖化血红蛋白等。所有仪器经校准,检测过程遵循标准化流程。
经培训的调查员通过面对面访谈收集受访者的人口学特征和生活方式信息,包括年龄、性别、民族(汉族、瑶族、其他)、文化程度(未接受正规教育、小学、初中及以上)、吸烟状况(定义:当前是否吸烟)、饮酒状况(定义为当前是否饮酒)、疾病患病状态。依据体格检查、药物治疗史及当地疾病预防控制中心慢性病监测系统电子病历综合判定:高血压依据《中国高血压防治指南》的诊断标准,收缩压≥140 mm Hg和(或)舒张压≥90 mm Hg,既往有高血压病史,且在进行降压治疗以及疾病预防控制中心慢性病监测系统的电子医疗记录即诊断为高血压。糖尿病参照《中国糖尿病防治指南(2024版)》标准,空腹血糖≥7.0mmol/L或糖化血红蛋白含量≥6.5%,或服用降糖药以及疾病预防控制中心慢性病监测系统的电子医疗记录。协变量的选择是基于既往文献[9]。
数据分析均采用SPSS 28.0与R 4.5.1软件完成。符合正态分布的连续变量以(均数±标准差)表示,组间比较用独立样本t检验;非正态分布数据以中位数(四分位间距)表示,组间比较用Wilcoxon或Kruskal-Wallis秩和检验。分类变量以样本量构成比表示,组间比较采用χ2检验。
横断面分析中,九种指数按四分位数分组,采用多因素logistic回归分析与HUA的相关性,计算比值比及95%置信区间;构建随机森林、逻辑回归、支持向量机(support vector machine,SVM)、极限梯度提升(extreme gradient boosting,XGBoost)和决策树五种机器学习模型,以受试者工作特征曲线下面积(area under the curve,AUC)评估模型性能,并基于最优性能模型,通过SHAP(Shapley additive explanations)值解析各指数对识别HUA风险的贡献度,通过特征重要性排序,筛选出识别HUA患病风险的最关键指数,作为前瞻性队列研究的核心指数。
前瞻性队列分析中,以横断面研究筛选的最关键指数,按四分位数分组,以最低分位组(Q1)为参照,采用修正泊松回归计算相对风险及其95%置信区间,验证最关键指数与HUA发病风险的关联;使用限制性立方样条(restricted cubic spline,RCS)分析最关键指数与HUA的剂量-反应关系。使用双分段线性回归模型及递归算法计算关联曲线的拐点,并使用对数似然比检验(log-likelihood ratio test,LRT)验证阈值效应的显著性,确定截断值。双侧检验,检验水准α=0.05。
3 071名受试者中,HUA组347例,非HUA组2 724例,HUA检出率为11.3%,其中男性、吸烟、饮酒、教育水平高、肥胖的研究对象中HUA检出率更高(P<0.001);HUA患者患有高血压、糖尿病的可能性更大(P<0.001)。见表1。
构建二个模型分析九种指数与HUA的关联,调整协变量(Model 2)后,eGDR、TyG、METS-IR、TG/HDL-C Ratio、TyG-BMI、TyG-WC、TyG-WHtR、ABSI、圆锥指数均与HUA存在关联(P<0.001)。见图1。
图2A为五种机器学习模型ROC曲线。如图2B所示,经AUC值排序,XGBoost模型的识别效能优于其他四种模型。基于XGBoost模型,采用SHAP值法解析各指标对HUA患病风险的贡献度、作用方向。变量重要性排序如图2C显示,TG/HDL-C Ratio的平均绝对SHAP值最高,为0.031,是本研究中识别HUA最重要的特征变量。蜂群图如2D所示,TG/HDL-C Ratio与HUA发病风险之间存在正相关关系。
本研究前瞻性队列共纳入1 583名基线无HUA受试者,随访期间新发HUA病例147例(9.28%)。基于前述TG/HDL-C Ratio被确定为九种指数中识别HUA发病的最优指标,采用修正泊松回归模型,校正协变量后,结果如图3所示,与Q1组相比,Q4组的受试者发生HUA的风险显著增加(RR=3.85,95% CI:2.38~6.21,P<0.001)。
RCS分析结果显示,TG/HDL-C Ratio与HUA呈非线性关联(Poverall<0.001,Pnonlinear<0.001),且二者之间存在阈值效应(LRT P<0.001)。TG/HDL-C Ratio的拐点为2.971。TG/HDL-C Ratio<2.971时,TG/HDL-C Ratio与HUA的风险增加相关(RR=1.88,95% CI:1.53~2.30,P<0.001)。TG/HDL-C Ratio≥2.971时,HUA的患病风险趋于平稳,无显著相关性(RR= 1.02,95%CI:1.00~1.05,P=0.106)。见图4。
本研究综合评估了eGDR、TyG、METS-IR、TG/HDL-C Ratio、ABSI、圆锥指数、TyG-BMI、TyG-WC、TyG-WHtR指标与HUA的关联,通过构建五种机器学习模型并采用SHAP可解释性分析,探索九种指数中识别HUA的最关键指数。结果表明TyG-BMI、TyG-WC、TyG-WHtR、 TyG、 METS-IR、 TG/HDL-C Ratio、ABSI、圆锥指数与HUA患病风险呈正相关,eGDR与HUA患病风险呈负相关。TG/HDL-C Ratio被确定为识别HUA的最关键指数。前瞻性队列中验证其与HUA为正相关,二者之间为非线性剂量-反应关系,确定其与HUA的截断值为2.917。
本研究各指标与HUA关联性的发现与既往研究结果一致,有研究指出,TyG、METS-IR、TG/HDL-C Ratio与HUA呈正相关[10]。TyG-WC、TyG-BMI和TyG-WHtR升高与HUA发生风险增加相关[11],ABSI、圆锥指数水平升高可增加HUA风险[12-13],而随eGDR水平升高,HUA的患病率下降,该发现与Wang[14]的结果一致。
本研究通过构建了多模型筛选与关键指标识别,TG/HDL-C Ratio被确定为识别该人群HUA的最关键指数。一项针对中老年群体的全国前瞻性队列研究及随访四年针对TG/HDL-C Ratio与HUA的前瞻性队列研究表明,TG/HDL-C Ratio与HUA存在显著相关性[9,15],与本研究结果类似。TG/HDL-C Ratio导致HUA机制主要包括脂质代谢和糖代谢紊乱。TG升高导致游离脂肪酸过量产生,促进肝脏脂肪酸合成与嘌呤再合成,加速尿酸生成[16];另外,高胰岛素血症状态下,胰岛素通过激活AKT信号通路上调URAT1蛋白表达,增加肾小管重吸收并降低肾脏对尿酸排泄来引起HUA[17-18];此外,胰岛素抵抗状态下脂解发生失调[19],脂蛋白脂肪酶活性降低[20],引起高脂血症,最终导致尿酸过量产生。
与Han等人[9]的发现一致,TG/HDL-C Ratio与HUA呈非线性关联。TG/HDL-C Ratio阈值为2.971,在TG/HDL-C Ratio<2.971区间,TG/HDL-C Ratio升高HUA的发病风险增加,此阶段机体正处于胰岛素抵抗逐渐加重,肾脏近端小管对尿酸的重吸收增强,因此随指标升高而呈上升;当TG/HDL-C Ratio≥2.971后,风险上升趋势减弱并趋于平稳,肾脏的尿酸重吸收转运蛋白已经处于饱和状态。肾脏对尿酸的重吸收由近端肾小管中的尿酸盐转运蛋白(如URAT1)介导[21],其对尿酸的转运具有限制能力,类似于膜蛋白动力学的饱和效应[22]。这一结果证实了TG/HDL-C Ratio识别HUA风险时存在非线性特征。本研究截断值2.971高于一项在年轻非肥胖人群研究中的截断值0.475[23]。中老年人群的生理性代谢功能减退,胰岛素敏感性下降以及肾储备功能下降[24-25],导致阈值较年轻人群发生上移。TG/HDL-C Ratio与HUA关联的截断值并非固定值,受年龄、体脂状态等因素影响,本研究针对广西县域中老年人群确定的截断值更具有更强的针对性临床指导价值。作为IR指数,TG/HDL-C Ratio计算公式源于常规血液指标检查,无需昂贵检测和复杂计算,易于基层医疗机构推广。结合本研究确定的截断值,临床中可以对血糖正常但TG/HDL-C Ratio>2.971的人群进行HUA风险的早期分层,在代谢损害发生前进行干预。
本次研究具有一些优点,本研究对九种胰岛素抵抗指标、脂质代谢指标和肥胖相关指标进行了综合比较,评估了其与HUA之间的关联,并利用机器学习模型结合SHAP方法筛选与识别关键指数。最后,通过前瞻性队列研究证实了TG/HDL-C Ratio指标与HUA存在因果关系。尽管如此,我们的研究存在以下局限性,尽管本研究的主要发现在该研究人群中表现一致,但其对其他地区或族群的适用性仍有待检验。另一个局限性是目前的研究是单中心研究,还需要前瞻性的多中心研究来进一步验证结果的稳健性。
  • 国家自然科学基金(82460629)
  • 广西科技厅重大专项资助项目(桂科AA22096026)
  • 广西研究生教育创新计划项目(YCSW2024460)
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2026年第53卷第15期
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doi: 10.20043/j.cnki.MPM.202601444
  • 接收时间:2026-01-25
  • 首发时间:2026-09-28
  • 出版时间:2026-08-10
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  • 收稿日期:2026-01-25
基金
国家自然科学基金(82460629)
广西科技厅重大专项资助项目(桂科AA22096026)
广西研究生教育创新计划项目(YCSW2024460)
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    桂林医科大学公共卫生学院,广西 桂林 541199

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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
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