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Article(id=1311350416240108345, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1311350193946194882, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202511453, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1764000000000, receivedDateStr=2025-11-25, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790579902944, onlineDateStr=2026-09-28, pubDate=1786291200000, pubDateStr=2026-08-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790579902944, onlineIssueDateStr=2026-09-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790579902944, creator=13701087609, updateTime=1790579902944, 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=2726, endPage=2732, ext={EN=ArticleExt(id=1311350418052047674, articleId=1311350416240108345, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Different parameters of the uric acid-to-high-density lipoprotein cholesterol ratio and the risk of diabetes mellitus in Chinese middle-aged and older adults, columnId=1240413921954295836, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods, runingTitle=null, highlight=null, articleAbstract=
Objective

To investigate the relationship between different uric acid-to-high-density lipoprotein cholesterol ratios (UHR) and new-onset diabetes in middle-aged and older individuals, providing a reference basis for diabetes prevention and treatment in this population.

Methods

Using data from the China Health and Retirement Longitudinal Study (CHARLS), we selected middle-aged and older adults who participated in five waves of the survey from 2011 to 2020. Restricted cubic spline (RCS) analysis was employed to examine the potential nonlinear associations of baseline UHR and cumulative UHR with diabetes risk. The UHR trajectory was categorized into three classes using the k-means clustering algorithm: low-level increasing class (Class 1), moderate-level increasing class (Class 2), and high-level decreasing class (Class 3). Cox proportional hazards regression models were then used to analyze the association between different UHR parameters and new-onset diabetes. Further subgroup analyses were conducted by sex, age, and household registration type, with interaction terms included to test for interactions. Logistic regression sensitivity analyses were performed to validate the robustness of the findings.

Results

This study included 4 638 participants, with 342 new cases of diabetes (7.37%)occurring during the 5-year follow-up period. RCS analysis revealed that higher baseline UHR and cumulative UHR levels were associated with increased diabetes risk in middle-aged and older adults. After adjusting for confounders, diabetes risk significantly increased in Class 2 and Class 3 groups compared to Class 1, with HRs of 1.42 (95% CI: 1.10-1.83) and 1.79 (95% CI: 1.27-2.53),respectively. Furthermore, compared with the lowest tertile group (T1) of baseline UHR and cumulative UHR, the adjusted HRs for the highest tertile group (T3) were 1.64 (95% CI: 1.21-2.23) and 1.60 (95% CI: 1.18-2.18), respectively.

Conclusion

Elevated UHR levels increase the risk of developing diabetes. Continuous monitoring of UHR and early intervention can help reduce the incidence of diabetes in middle-aged and older adults.

, authors=Si-qi LIU, Si-han LI, authorsList=Si-qi LIU, Si-han LI, authorCompany=null, correspAuthors=Si-han LI, 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=1311350419213869891, articleId=1311350416240108345, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=中国中老年人不同尿酸和高密度脂蛋白胆固醇比值参数与糖尿病发病风险研究, 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

探讨不同尿酸和高密度脂蛋白胆固醇比值（uric acid and high-density lipoprotein cholesterol ratio,UHR）与中老年人新发糖尿病的关系，为中老年人糖尿病防治提供参考依据。

方法

基于中国健康与养老追踪调查数据，选取参加2011—2020年五次调查的中老年人。采用限制性立方样条（restricted cubic splines,RCS）分析基线UHR和累积UHR与糖尿病发病风险间潜在的非线性关联。利用K-means聚类算法将UHR变化轨迹分为三类：低水平增长类（Class 1）、中等水平增长类（Class 2）和高水平下降类（Class 3），并采用Cox比例风险回归模型分析不同UHR参数与新发糖尿病的关联。进一步按性别、年龄、户口类型进行亚组分析，并通过纳入交互项检验交互作用。采用logistic回归进行敏感性分析以验证结果的稳健性。

结果

本研究共纳入4 638名参与者，五年随访期间，新发糖尿病342例（7.37%）。RCS分析显示，基线UHR和累积UHR水平越高，中老年人患糖尿病的风险越高。在控制混杂因素后，与Class 1类相比，Class 2类和Class 3类的糖尿病发病风险显著增加，其HR分别为1.42（95%CI：1.10～1.83）和1.79（95%CI：1.27～2.53）。此外，与基线UHR和累积UHR的最低三分位数组（T1）相比，最高三分位数组（T3）调整后HR分别为1.64（95%CI：1.21～2.23）和1.60（95%CI：1.18～2.18）。

结论

UHR水平升高会增加糖尿病发病风险。持续监测UHR并进行早期干预，有助于降低中老年人糖尿病的发病率。

, authors=刘斯琪, 黎思含, authorsList=刘斯琪, 黎思含, authorCompany=null, correspAuthors=黎思含, authorNote=

刘斯琪（1991—），女，本科，护师 ，研究方向：慢病管理

, correspAuthorsNote=
黎思含，E-mail：
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刘斯琪（1991—），女，本科，护师 ，研究方向：慢病管理

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Frontiers in Endocrinology, 2024, 15: 1272314., articleTitle=Association between high-density lipoprotein cholesterol and type 2 diabetes mellitus:dual evidence from NHANES database and Mendelian randomization analysis, refAbstract=null), Reference(id=1311350425119449978, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350416240108345, doi=null, pmid=null, pmcid=null, year=2024, volume=15, issue=1, pageStart=113, pageEnd=120, url=null, language=null, rfNumber=[19], rfOrder=18, authorNames=Zhou XH, Xu J, journalName=Journal of Diabetes Investigation, refType=null, unstructuredReference=Zhou XH, Xu J. Association between serum uric acid-to-high- density lipoprotein cholesterol ratio and insulin resistance in patients with type 2 diabetes mellitus[J]. Journal of Diabetes Investigation, 2024, 15(1): 113-120., articleTitle=Association between serum uric acid-to-high- density lipoprotein cholesterol ratio and insulin resistance in patients with type 2 diabetes mellitus, refAbstract=null), Reference(id=1311350425211724667, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350416240108345, doi=null, pmid=null, pmcid=null, year=2024, volume=17, issue=0, pageStart=121, pageEnd=129, url=null, language=null, rfNumber=[20], rfOrder=19, authorNames=Wang YS, Xu YT, Hu T, journalName=Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, refType=null, unstructuredReference=Wang YS, Xu YT, Hu T, et al. Associations of serum uric acid to High-Density lipoprotein cholesterol ratio with trunk fat mass and visceral fat accumulation[J]. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, 2024, 17(0): 121-129., articleTitle=Associations of serum uric acid to High-Density lipoprotein cholesterol ratio with trunk fat mass and visceral fat accumulation, refAbstract=null), Reference(id=1311350425274639228, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350416240108345, doi=null, pmid=null, pmcid=null, year=2024, volume=3, issue=5, pageStart=18, pageEnd=null, url=null, language=null, rfNumber=[21], rfOrder=20, authorNames=Liang N, Yuan X, Zhang L, journalName=Life Metab, refType=null, unstructuredReference=Liang N, Yuan X, Zhang L, et al. Fatty acid oxidation-induced HIF-1α activation facilitates hepatic urate synthesis through upregulating NT5C2 and XDH[J]. Life Metab, 2024, 3(5): 18., articleTitle=Fatty acid oxidation-induced HIF-1α activation facilitates hepatic urate synthesis through upregulating NT5C2 and XDH, refAbstract=null), Reference(id=1311350426876863360, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350416240108345, doi=null, pmid=null, pmcid=null, year=2021, volume=175, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[22], rfOrder=21, authorNames=Kim MK, Han K, Koh ES, journalName=Diabetes Research and Clinical Practice, refType=null, unstructuredReference=Kim MK, Han K, Koh ES, et al. Cumulative exposure to impaired fasting glucose and future risk of type 2 diabetes mellitus[J].Diabetes Research and Clinical Practice, 2021, 175: 108799., articleTitle=Cumulative exposure to impaired fasting glucose and future risk of type 2 diabetes mellitus, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1311350419423585092, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350416240108345, xref=null, ext=[AuthorCompanyExt(id=1311350419431973701, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350416240108345, companyId=1311350419423585092, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Shengjing Hospital of China Medical University, Shenyang, Liaoning 110001, China), AuthorCompanyExt(id=1311350419440362310, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350416240108345, companyId=1311350419423585092, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=中国医科大学附属盛京医院，辽宁 沈阳 110001)])], figs=[ArticleFig(id=1311350420723819353, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350416240108345, language=EN, label=Figure 1, caption=Dose-response relationship between UHR and cumulative UHR and diabetes risk, figureFileSmall=0x90Fupro6fTkhAgaP2oqw==, figureFileBig=V/euVreqgPfKzrKLJPq5/Q==, tableContent=null), ArticleFig(id=1311350420795122522, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350416240108345, language=CN, label=图1, caption=UHR和累积UHR与糖尿病风险的剂量反应关系

注：A为基线UHR与糖尿病风险；B为累积UHR与糖尿病风险。

, figureFileSmall=0x90Fupro6fTkhAgaP2oqw==, figureFileBig=V/euVreqgPfKzrKLJPq5/Q==, tableContent=null), ArticleFig(id=1311350422544147291, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350416240108345, language=EN, label=Figure 2, caption=K-means clustering of UHR trajectories, figureFileSmall=fXKekHwD7yrNM67YzMBPWA==, figureFileBig=3N0nNRkSdpwMopofaBfgmA==, tableContent=null), ArticleFig(id=1311350422598673244, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350416240108345, language=CN, label=图2, caption=UHR变化轨迹的K-means聚类结果, figureFileSmall=fXKekHwD7yrNM67YzMBPWA==, figureFileBig=3N0nNRkSdpwMopofaBfgmA==, tableContent=null), ArticleFig(id=1311350422674170717, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350416240108345, language=EN, label=Table 1, caption=

Basic characteristics of study participants ［（）， n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
基本特征总人数糖尿病t/χ2值P值
否是
年龄(岁)57.89±8.3957.89±8.4258.01±8.110.2540.799
血清尿酸 (μmol/L)257.58±70.70256.63±70.36269.50±73.9110.520<0.001
HDL-C (mmol/L)1.35±0.381.36±0.381.28±0.3911.889<0.001
UHR0.09±0.040.09±0.040.10±0.0420.842<0.001
CRP (mg/L)22.13±63.0121.91±63.4024.88±57.970.7030.402
血尿素氮 (mmol/L)5.56±1.575.55±1.575.63±1.580.7890.374
甘油三酯 (mmol/L)4.96±0.974.94±0.965.15±1.0414.474<0.001
总胆固醇 (mmol/L)1.35±0.841.33±0.821.57±0.9625.686<0.001
性别0.8620.353
男2 045(44.09)1 886(43.90)159(46.49)
女2 593(55.91)2 410(56.10)183(53.51)
户口类型0.4630.496
农业3 956(85.30)3 660(85.20)296(86.55)
非农业682(14.70)636(14.80)46(13.45)
学历4.9340.085
小学以下2 135(46.03)1 958(45.58)177(51.75)
小学初中2 043(44.05)1 907(44.39)136(39.77)
≥高中460(9.92)431(10.03)29(8.48)
婚姻状况4.6960.030
已婚同居4 004(86.33)3 722(86.64)282(82.46)
其他634(13.67)574(13.36)60(17.54)
吸烟1.6910.193
是1 711(36.89)1 596(37.15)115(33.63)
否2 927(63.11)2 700(62.85)227(66.37)
饮酒频率（次/月）0.1520.927
≥11 158(24.97)1 072(24.95)86(25.15)
<1396(8.54)365(8.50)31(9.06)
不饮酒3 084(66.49)2 859(66.55)225(65.79)
高血压22.999<0.001
否3 070(66.19)2 884(67.13)186(54.39)
是1 568(33.81)1 412(32.87)156(45.61)
心脏病18.502<0.001
否4 155(89.59)3 872(90.13)283(82.75)
是483(10.41)424(9.87)59(17.25)
基线UHR三分位数22.026<0.001
T11 545(33.31)1 459(33.96)86(25.15)
T21 547(33.35)1 443(33.59)104(30.41)
T31 546(33.33)1 394(32.45)152(44.44)
累积UHR三分位数20.268<0.001
T11 545(33.31)1 461(34.01)84(24.56)
T21 546(33.33)1 437(33.45)109(31.87)
T31 547(33.35)1 398(32.54)149(43.57)
UHR变化轨迹25.452<0.001
Class 12 227(48.02)2 102(48.93)125(36.55)
Class 21 776(38.29)1 630(37.94)146(42.69)
Class 3635(13.69)564(13.13)71(20.76)
), ArticleFig(id=1311350422795805534, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350416240108345, language=CN, label=表1, caption=

研究对象基本特征 ［（），n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
基本特征总人数糖尿病t/χ2值P值
否是
年龄(岁)57.89±8.3957.89±8.4258.01±8.110.2540.799
血清尿酸 (μmol/L)257.58±70.70256.63±70.36269.50±73.9110.520<0.001
HDL-C (mmol/L)1.35±0.381.36±0.381.28±0.3911.889<0.001
UHR0.09±0.040.09±0.040.10±0.0420.842<0.001
CRP (mg/L)22.13±63.0121.91±63.4024.88±57.970.7030.402
血尿素氮 (mmol/L)5.56±1.575.55±1.575.63±1.580.7890.374
甘油三酯 (mmol/L)4.96±0.974.94±0.965.15±1.0414.474<0.001
总胆固醇 (mmol/L)1.35±0.841.33±0.821.57±0.9625.686<0.001
性别0.8620.353
男2 045(44.09)1 886(43.90)159(46.49)
女2 593(55.91)2 410(56.10)183(53.51)
户口类型0.4630.496
农业3 956(85.30)3 660(85.20)296(86.55)
非农业682(14.70)636(14.80)46(13.45)
学历4.9340.085
小学以下2 135(46.03)1 958(45.58)177(51.75)
小学初中2 043(44.05)1 907(44.39)136(39.77)
≥高中460(9.92)431(10.03)29(8.48)
婚姻状况4.6960.030
已婚同居4 004(86.33)3 722(86.64)282(82.46)
其他634(13.67)574(13.36)60(17.54)
吸烟1.6910.193
是1 711(36.89)1 596(37.15)115(33.63)
否2 927(63.11)2 700(62.85)227(66.37)
饮酒频率（次/月）0.1520.927
≥11 158(24.97)1 072(24.95)86(25.15)
<1396(8.54)365(8.50)31(9.06)
不饮酒3 084(66.49)2 859(66.55)225(65.79)
高血压22.999<0.001
否3 070(66.19)2 884(67.13)186(54.39)
是1 568(33.81)1 412(32.87)156(45.61)
心脏病18.502<0.001
否4 155(89.59)3 872(90.13)283(82.75)
是483(10.41)424(9.87)59(17.25)
基线UHR三分位数22.026<0.001
T11 545(33.31)1 459(33.96)86(25.15)
T21 547(33.35)1 443(33.59)104(30.41)
T31 546(33.33)1 394(32.45)152(44.44)
累积UHR三分位数20.268<0.001
T11 545(33.31)1 461(34.01)84(24.56)
T21 546(33.33)1 437(33.45)109(31.87)
T31 547(33.35)1 398(32.54)149(43.57)
UHR变化轨迹25.452<0.001
Class 12 227(48.02)2 102(48.93)125(36.55)
Class 21 776(38.29)1 630(37.94)146(42.69)
Class 3635(13.69)564(13.13)71(20.76)
), ArticleFig(id=1311350422875497311, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350416240108345, language=EN, label=Table 2, caption=

Relationship between baseline and cumulative UHR and new-onset diabetes

, figureFileSmall=null, figureFileBig=null, tableContent=
UHR参数糖尿病人数/总人数模型1模型2
HR (95% CI)P值HR (95% CI)P值
基线UHR三分位数
T186/1 5451.0 (ref)1.0 (ref)
T2104/1 5471.22(0.91～1.63)0.1781.19(0.89～1.60)0.239
T3152/1 5461.82(1.38～2.40)<0.0011.64(1.21～2.23)0.001
累积UHR三分位数
T184/1 5451.0 (ref)1.0 (ref)
T2109/1 5461.32(0.99～1.76)0.0601.27(0.95～1.70)0.108
T3149/1 5471.84(1.39～2.44)<0.0011.60(1.18～2.18)0.002
), ArticleFig(id=1311350422946800480, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350416240108345, language=CN, label=表2, caption=

基线及累积UHR与新发糖尿病的关系

, figureFileSmall=null, figureFileBig=null, tableContent=
UHR参数糖尿病人数/总人数模型1模型2
HR (95% CI)P值HR (95% CI)P值
基线UHR三分位数
T186/1 5451.0 (ref)1.0 (ref)
T2104/1 5471.22(0.91～1.63)0.1781.19(0.89～1.60)0.239
T3152/1 5461.82(1.38～2.40)<0.0011.64(1.21～2.23)0.001
累积UHR三分位数
T184/1 5451.0 (ref)1.0 (ref)
T2109/1 5461.32(0.99～1.76)0.0601.27(0.95～1.70)0.108
T3149/1 5471.84(1.39～2.44)<0.0011.60(1.18～2.18)0.002
), ArticleFig(id=1311350423026492257, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350416240108345, language=EN, label=Table 3, caption=

Relationship between UHR clustering and new-onset diabetes

, figureFileSmall=null, figureFileBig=null, tableContent=
UHR变化轨迹糖尿病人数/总人数模型 1模型2
HR (95% CI)P值HR (95% CI)P值
Class 1125/2 2271.0 (ref)1.0 (ref)
Class 2146/1 7761.51(1.18～1.93)<0.0011.42(1.10～1.83)0.006
Class 371/6352.11(1.55～2.88)<0.0011.79(1.27～2.53)0.001
), ArticleFig(id=1311350423085212514, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350416240108345, language=CN, label=表3, caption=

UHR聚类与新发糖尿病关系

, figureFileSmall=null, figureFileBig=null, tableContent=
UHR变化轨迹糖尿病人数/总人数模型 1模型2
HR (95% CI)P值HR (95% CI)P值
Class 1125/2 2271.0 (ref)1.0 (ref)
Class 2146/1 7761.51(1.18～1.93)<0.0011.42(1.10～1.83)0.006
Class 371/6352.11(1.55～2.88)<0.0011.79(1.27～2.53)0.001
), ArticleFig(id=1311350423152321379, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350416240108345, language=EN, label=Table 4, caption=

Subgroup analysis of the association between UHR trajectory classes and new-onset diabetes ［HR (95% CI)］

, figureFileSmall=null, figureFileBig=null, tableContent=
亚组分析Class 1Class 2P值Class 3P值交互效应P值
性别0.745
男性1.0 (ref)1.27(0.84～1.93)0.2521.63(1.02～2.61)0.040
女性1.0 (ref)1.51(1.09～2.08)0.0131.68(0.95～2.97)0.072
年龄（岁）0.063
<601.0 (ref)1.76(1.27～2.46)0.0012.24(1.42～3.52)<0.001
≥601.0 (ref)1.06(0.71～1.57)0.7911.30(0.76～2.23)0.334
户口类型0.451
农业1.0 (ref)1.34(1.02～1.76)0.0331.74(1.20～2.51)0.003
非农业1.0 (ref)2.30(1.03～5.15)0.0432.62(0.94～7.27)0.064
), ArticleFig(id=1311350423232013156, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350416240108345, language=CN, label=表4, caption=

不同UHR变化轨迹与新发糖尿病关联的亚组分析［HR（95%CI）］

, figureFileSmall=null, figureFileBig=null, tableContent=
亚组分析Class 1Class 2P值Class 3P值交互效应P值
性别0.745
男性1.0 (ref)1.27(0.84～1.93)0.2521.63(1.02～2.61)0.040
女性1.0 (ref)1.51(1.09～2.08)0.0131.68(0.95～2.97)0.072
年龄（岁）0.063
<601.0 (ref)1.76(1.27～2.46)0.0012.24(1.42～3.52)<0.001
≥601.0 (ref)1.06(0.71～1.57)0.7911.30(0.76～2.23)0.334
户口类型0.451
农业1.0 (ref)1.34(1.02～1.76)0.0331.74(1.20～2.51)0.003
非农业1.0 (ref)2.30(1.03～5.15)0.0432.62(0.94～7.27)0.064
), ArticleFig(id=1311350423311704933, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350416240108345, language=EN, label=Table 5, caption=

Sensitivity analysis of the association between UHR trajectory classes and new-onset diabetes using logistic regression

, figureFileSmall=null, figureFileBig=null, tableContent=
UHR变化轨迹糖尿病人数/总人数模型 1模型2
OR (95% CI)P值OR (95% CI)P值
Class 1125/2 2271.0 (ref)1.0 (ref)
Class 2146/1 7761.54(1.20～1.99)0.0011.45(1.11～1.90)0.006
Class 371/6352.21(1.60～3.06)<0.0011.88(1.30～2.70)0.001
), ArticleFig(id=1311350423370425190, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350416240108345, language=CN, label=表5, caption=

UHR变化轨迹与新发糖尿病关联的敏感性分析（logistic回归）

, figureFileSmall=null, figureFileBig=null, tableContent=
UHR变化轨迹糖尿病人数/总人数模型 1模型2
OR (95% CI)P值OR (95% CI)P值
Class 1125/2 2271.0 (ref)1.0 (ref)
Class 2146/1 7761.54(1.20～1.99)0.0011.45(1.11～1.90)0.006
Class 371/6352.21(1.60～3.06)<0.0011.88(1.30～2.70)0.001
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中国中老年人不同尿酸和高密度脂蛋白胆固醇比值参数与糖尿病发病风险研究
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刘斯琪 , 黎思含
现代预防医学 | 流行病与统计方法 2026,53(15): 2726-2732
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现代预防医学 |流行病与统计方法 2026 , 53 (15) : 2726 -2732
中国中老年人不同尿酸和高密度脂蛋白胆固醇比值参数与糖尿病发病风险研究
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刘斯琪（1991—），女，本科，护师 ，研究方向：慢病管理

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刘斯琪（1991—），女，本科，护师 ，研究方向：慢病管理

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刘斯琪, 黎思含
作者信息
  • 中国医科大学附属盛京医院，辽宁 沈阳 110001
通讯作者:
黎思含，E-mail：
作者简介:

刘斯琪（1991—），女，本科，护师 ，研究方向：慢病管理

Different parameters of the uric acid-to-high-density lipoprotein cholesterol ratio and the risk of diabetes mellitus in Chinese middle-aged and older adults
Si-qi LIU, Si-han LI
Affiliations
  • Shengjing Hospital of China Medical University, Shenyang, Liaoning 110001, China
出版时间: 2026-08-10 doi: 10.20043/j.cnki.MPM.202511453
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摘要
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目的

探讨不同尿酸和高密度脂蛋白胆固醇比值（uric acid and high-density lipoprotein cholesterol ratio,UHR）与中老年人新发糖尿病的关系，为中老年人糖尿病防治提供参考依据。

方法

基于中国健康与养老追踪调查数据，选取参加2011—2020年五次调查的中老年人。采用限制性立方样条（restricted cubic splines,RCS）分析基线UHR和累积UHR与糖尿病发病风险间潜在的非线性关联。利用K-means聚类算法将UHR变化轨迹分为三类：低水平增长类（Class 1）、中等水平增长类（Class 2）和高水平下降类（Class 3），并采用Cox比例风险回归模型分析不同UHR参数与新发糖尿病的关联。进一步按性别、年龄、户口类型进行亚组分析，并通过纳入交互项检验交互作用。采用logistic回归进行敏感性分析以验证结果的稳健性。

结果

本研究共纳入4 638名参与者，五年随访期间，新发糖尿病342例（7.37%）。RCS分析显示，基线UHR和累积UHR水平越高，中老年人患糖尿病的风险越高。在控制混杂因素后，与Class 1类相比，Class 2类和Class 3类的糖尿病发病风险显著增加，其HR分别为1.42（95%CI：1.10～1.83）和1.79（95%CI：1.27～2.53）。此外，与基线UHR和累积UHR的最低三分位数组（T1）相比，最高三分位数组（T3）调整后HR分别为1.64（95%CI：1.21～2.23）和1.60（95%CI：1.18～2.18）。

结论

UHR水平升高会增加糖尿病发病风险。持续监测UHR并进行早期干预，有助于降低中老年人糖尿病的发病率。

糖尿病  /  尿酸和高密度脂蛋白胆固醇比值  /  中老年人  /  队列研究
Objective

To investigate the relationship between different uric acid-to-high-density lipoprotein cholesterol ratios (UHR) and new-onset diabetes in middle-aged and older individuals, providing a reference basis for diabetes prevention and treatment in this population.

Methods

Using data from the China Health and Retirement Longitudinal Study (CHARLS), we selected middle-aged and older adults who participated in five waves of the survey from 2011 to 2020. Restricted cubic spline (RCS) analysis was employed to examine the potential nonlinear associations of baseline UHR and cumulative UHR with diabetes risk. The UHR trajectory was categorized into three classes using the k-means clustering algorithm: low-level increasing class (Class 1), moderate-level increasing class (Class 2), and high-level decreasing class (Class 3). Cox proportional hazards regression models were then used to analyze the association between different UHR parameters and new-onset diabetes. Further subgroup analyses were conducted by sex, age, and household registration type, with interaction terms included to test for interactions. Logistic regression sensitivity analyses were performed to validate the robustness of the findings.

Results

This study included 4 638 participants, with 342 new cases of diabetes (7.37%)occurring during the 5-year follow-up period. RCS analysis revealed that higher baseline UHR and cumulative UHR levels were associated with increased diabetes risk in middle-aged and older adults. After adjusting for confounders, diabetes risk significantly increased in Class 2 and Class 3 groups compared to Class 1, with HRs of 1.42 (95% CI: 1.10-1.83) and 1.79 (95% CI: 1.27-2.53),respectively. Furthermore, compared with the lowest tertile group (T1) of baseline UHR and cumulative UHR, the adjusted HRs for the highest tertile group (T3) were 1.64 (95% CI: 1.21-2.23) and 1.60 (95% CI: 1.18-2.18), respectively.

Conclusion

Elevated UHR levels increase the risk of developing diabetes. Continuous monitoring of UHR and early intervention can help reduce the incidence of diabetes in middle-aged and older adults.

Diabetes mellitus  /  Uric acid-to-high-density lipoprotein cholesterol ratio  /  Middle-aged and older adults  /  Cohort study
刘斯琪, 黎思含. 中国中老年人不同尿酸和高密度脂蛋白胆固醇比值参数与糖尿病发病风险研究. 现代预防医学, 2026 , 53 (15) : 2726 -2732 . DOI: 10.20043/j.cnki.MPM.202511453
Si-qi LIU, Si-han LI. Different parameters of the uric acid-to-high-density lipoprotein cholesterol ratio and the risk of diabetes mellitus in Chinese middle-aged and older adults[J]. Modern Preventive Medicine, 2026 , 53 (15) : 2726 -2732 . DOI: 10.20043/j.cnki.MPM.202511453
正文
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糖尿病是全球最常见的慢性病之一，已成为威胁人类健康的重大公共卫生挑战[1]。近年来，糖尿病患病率持续上升。据国际糖尿病联盟数据显示，2024年全球糖尿病患者已超5.89亿，预计到2050年将达8.52亿[2]。中国是糖尿病患病人数最多的国家，2024年患者约1.48亿，预计到2050年将超1.68亿[2]。糖尿病严重危害人民健康，因此，早期诊断与干预对于预防或延缓糖尿病发生至关重要。
血清尿酸与高密度脂蛋白胆固醇（high density lipoprotein cholesterol，HDL-C）的比值（uric acid and HDL-C ratio，UHR）作为一种新型炎症和代谢指标，近年来受到广泛关注[3-4]。血清尿酸 作为嘌呤代谢的终末产物，其水平升高往往与胰岛素抵抗、慢性炎症相关[5]；同时还会抑制一氧化氮生成、刺激血管平滑肌细胞增殖、加速动脉粥样硬化进程，进而增加糖尿病及心血管疾病的发病风险[6]。而HDL-C因其具有抗氧化、抗炎及胆固醇逆向转运能力，被认为是糖尿病的保护因素[7]。UHR作为血清尿酸与HDL-C的比值，能够捕捉两者水平变化的协同影响，更精准地反映个体代谢状态，从而有效预测糖尿病及其他代谢性疾病的发生风险[8-9]。然而，当前多数研究仅关注基线UHR水平，对于UHR与新发糖尿病在单一时间尺度、多个时间尺度及变化轨迹上的关联尚不明确。因此，需深入探究不同UHR参数对糖尿病发病风险的影响，以期为中国中老年人新发糖尿病的预警与干预提供新视角。
1　 资料与方法
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1.1　研究对象
本研究数据来源于中国健康与养老追踪调查（China Health and Retirement Longitudinal Study，CHARLS）。这是一项全国性的纵向调查，覆盖了中国28个省份、150个县及450个社区或村庄，共收集17 707名中老年人相关信息[10]。截至当前，CHARLS分别在2011、2013、2015、2018和2020年完成五轮调查，本研究使用了这五轮全部调查数据。
本研究排除了在2011—2015年调查期间已确诊为糖尿病的参与者，以及血清尿酸和HDL-C等关键数据缺失的样本。还剔除了年龄<45岁、性别、学历等基本信息缺失以及在随访过程中失访的研究对象。最终共纳入了4 638名研究对象。CHARLS研究已获得北京大学医学伦理委员会批准（批号:IRB00001052-11015），所有参与者均在知情的前提下签署了同意书。
1.2　相关定义
糖尿病的诊断标准如下：（1）空腹血糖浓度≥126 mg/dl或糖化血红蛋白水平≥6.5%，即确诊为糖尿病[11]；（2）结合 CHARLS 问卷数据，若参与者报告曾被医生确诊糖尿病或目前正在接受相关治疗，亦归类为糖尿病患者。
基线UHR：血清尿酸与HDL-C的比值[12]。
累积UHR：采用时间加权平均法计算2011—2015年UHR的累积暴露水平，计算公式为：累积UHR=(UHR2011+UHR2015) / 2×(2015-2011)[13]。
1.3　调查内容
（1）人口统计学与生活方式信息：性别、年龄、户口类型、教育程度、婚姻状况、吸烟和饮酒情况等基本信息。（2）疾病史与用药史：详细记录了参与者是否患有心脏病、高血压、糖尿病等常见慢性疾病，以及针对慢性病所使用的药物情况。（3）实验室检测数据：采集空腹血糖、血清尿酸、HDL-C、C反应蛋白（C-reactive protein ，CRP）、血尿素氮、甘油三酯和总胆固醇等关键生物标志物。
1.4　统计学方法
符合正态分布的计量数据以（均数±标准差）描述，非正态分布的计量数据采用中位数和四分位数间距描述；计数数据使用频数和百分比描述。采用t检验、Wilcoxon 秩和检验或χ2检验比较组间差异。
为全面评估不同维度的UHR参数与糖尿病风险的关联，本研究采用两种策略对UHR进行分组。首先，对于基线UHR和累积UHR，先通过上述公式计算所有研究对象的数值（基线UHR为单次检测值，累积UHR为时间加权计算值），再采用P分位数法将基线UHR和累积UHR值按升序排列，以33.33%和66.67%为临界值将研究人群分为最低三分位数组（T1）、中间三分位数组（T2）和最高三分位数组（T3），每组样本量基本均衡，以此分析基线UHR和累积UHR不同暴露水平与新发糖尿病的关联。其次，采用K-means聚类算法结合肘部法确定最佳聚类数（K=3），对研究对象2011—2015年的UHR动态变化轨迹进行分层分析，聚类结果见图2[14]。
采用Cox比例风险回归模型分析不同UHR参数与新发糖尿病的关联；利用限制性立方样条（restricted cubic spline，RCS）探索两者间潜在的非线性关联。通过亚组分析（按性别、年龄、户口类型分层）评估上述关联的一致性，并在模型中纳入交互项，结合似然比检验验证交互作用的显著性。同时，采用logistic回归开展敏感性分析以验证结果稳定性。统计分析均使用SPSS 26.0及R软件（4.2.2 版），检验水准α=0.05。
2　结果
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2.1　研究对象基本情况
本研究共纳入4 638名参与者，平均年龄为（57.89±8.39）岁，其中男性2 045人，占比44.09%。在为期5年的随访中，共有342例参与者新发糖尿病，发病率为7.37%。与非糖尿病参与者相比，新发糖尿病患者基线年龄较大、学历较低（小学及以下）、多为农业户口、未婚，有更高的CRP、血尿素氮、甘油三酯和总胆固醇水平，血清尿酸水平亦显著升高，而HDL-C水平则显著降低，以及更高的高血压和心脏病患病率。基线和累积UHR三分位数最高者以及K-means聚类分析中的Class 3类个体，糖尿病发病风险也较高。所有研究对象的人口统计学和临床特征见表1。
2.2　基线UHR和累积UHR与新发糖尿病的关系
将基线UHR和累积UHR分别按三分位数分为T1组、T2组、T3组。在调整性别、年龄和学历等潜在混杂因素后（模型2），基线UHR中T3组的糖尿病发病风险显著高于T1组（HR=1.64，95%CI：1.21～2.23）。累积UHR水平中，与T1组相比，T3组糖尿病发病风险增加了60%，其HR和95%CI为1.60（1.18～2.18）。见表2。
采用RCS分析基线UHR及累积UHR与糖尿病风险之间的剂量反应关系。调整性别、年龄、户口类型、学历、婚姻状况、吸烟、饮酒频率、CRP、血尿素氮、甘油三酯、总胆固醇、高血压以及心脏病后，结果显示，随着基线UHR和累积UHR水平的升高，中老年人群患糖尿病的风险呈显著线性增长趋势（P非线性>0.05）。见图1。
2.3　UHR变化轨迹与新发糖尿病的关系
为探究UHR动态变化轨迹与中老年人新发糖尿病风险的关联，本研究采用K-means聚类算法对4 638名研究对象2011—2015年的UHR动态变化趋势进行分类。采用肘部法以总误差平方和（sum of squared errors,SSE）衡量类内数据聚集程度，确定最佳聚类数：当SSE随聚类数（K）增加的下降趋势由陡峭转为平缓时，对应的K值为最优聚类数。本研究中SSE-K曲线在K=3时出现明显“肘部”拐点，见图2，提示将UHR变化轨迹分为3类时聚类效果最优，类内同质性最高且类间异质性最显著。
基于上述聚类标准，本研究将4 638名研究对象分为三类：低水平增长类（Class 1）2 227人（占比48.02%）、中等水平增长类（Class 2）1 776人（占比38.29%）以及高水平下降类（Class 3）635人（占比13.69%）。三类人群的发病率分别为5.61%、8.22%、11.18%。在调整性别、年龄、学历、户口类型等混杂因素后(模型2)，以Class 1类作为参照，Class 2类和Class 3类发生糖尿病风险分别上升42%和79%，其HR和95%CI分别为1.42(1.10～1.83)和1.79(1.27～2.53)。见表3。
2.4　中国中老年人不同 UHR 变化轨迹与新发糖尿病发病风险关联的亚组分析
基于性别、年龄和户口类型进行分层分析，探讨不同层级中UHR变化轨迹与糖尿病风险之间的关系。结果显示，在男性群体中，Class 3类与糖尿病风险增加显著相关（P<0.05）；在女性群体中，Class 2类则与糖尿病风险增加显著相关（P<0.05）。按年龄分层分析发现，在年龄<60岁的参与者中，Class 2和Class 3类均显著增加了糖尿病发病风险（P<0.05），而在年龄≥60岁的参与者中，未观察到Class 2和Class 3类与糖尿病发病风险之间的显著关联。在农业户口中，Class 2类和Class 3类均显著增加了糖尿病发病风险（P<0.05），而在非农业户口中，仅观察到Class 2类会增加糖尿病的发病风险（P<0.05）。性别、年龄和户口类型与UHR变化轨迹间均无显著相乘交互效应（P>0.05）。见表4。
2.5　UHR变化轨迹与新发糖尿病发病风险关联的敏感性分析（logistic 回归）
为了进一步评估研究结果的稳健性，采用logistic回归模型进行了敏感性分析。结果如表5所示，在调整了性别、年龄、户口类型、学历、婚姻状况、吸烟、饮酒频率、CRP、血尿素氮、甘油三酯、总胆固醇、高血压以及心脏病协变量后，与Class1类相比，Class2类（OR=1.45，95% CI：1.11～1.90）和Class3类（OR=1.88，95% CI：1.30～2.70）发生糖尿病的风险仍显著增加（P<0.05）。
3　讨论
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本研究基于CHARLS数据，系统探讨了基线UHR、累积 UHR 及其动态变化轨迹与中老年人新发糖尿病风险的关联，同时通过亚组分析和敏感性分析验证关联的异质性和结果稳健性。
结果发现，与T1组相比，基线UHR T3组HR=1.64（95%CI:1.21～2.23），累积UHR T3组HR=1.60（95%CI:1.18～2.18）。在调整性别、年龄、吸烟、饮酒频率等混杂因素后，该关联仍然稳健，这与既往研究结论一致[15-16]。从机制上看，血清尿酸作为嘌呤代谢的终末产物，其水平升高可促进炎症因子（如CRP）释放，损伤胰岛β细胞功能，从而直接参与糖尿病病理进程[17]；而HDL-C具有抗炎和抗氧化作用，其水平下降往往伴随代谢功能紊乱加剧与胰岛素抵抗加重，进而提升糖尿病发生风险[18]。UHR作为整合血清尿酸促炎促氧化作用与HDL-C抗损伤作用的复合指标，既能综合反映两种代谢标志物的协同影响，又能动态体现代谢异常的累积效应，因此有望成为糖尿病及其他代谢性疾病风险评估的重要生物学标志物[19]。
UHR变化轨迹分析进一步揭示了UHR动态演变与糖尿病发病风险的密切关联。与Class 1类相比，Class 3类的新发糖尿病风险显著升高79%。从病理生理学角度，Class 3类人群存在尿酸水平相对较高且波动、HDL-C水平下降的双重失衡，提示脂质代谢与嘌呤代谢协同异常，这种剧烈的动态变化可能早于空腹血糖异常，成为糖尿病前期的预警信号[20]。值得注意的是，Class 2类的糖尿病发病风险亦显著上升42%，这一发现同样不容忽视。Class 2类人群的特征在于UHR水平呈持续、温和的上升趋势，虽然其波动幅度不及Class 3类剧烈，但这种温和式的长期暴露同样构成了不容忽视的代谢威胁。持续的UHR升高反映了体内促炎（血清尿酸升高）与抗炎（HDL-C降低）平衡的持续倾斜，这种慢性、低度的炎症状态足以通过氧化应激等途径损伤胰岛β细胞功能，并逐步加重外周组织的胰岛素抵抗[17-18]。这一结果与累积UHR的发现相互印证，共同强调了UHR长期暴露的“累积效应”在糖尿病发生发展中的核心作用[21-22]。即使是中等水平的持续偏离正常轨迹，日积月累的代谢负荷也足以跨越疾病发生的阈值。有趣的是，亚组分析结果为此提供了更精细的视角：在女性群体中，Class 2类即表现出显著的糖尿病风险（HR=1.51），而在男性群体中，风险的显著升高则推迟至波动更为剧烈的Class 3类（HR=1.63）。这种性别差异可能归因于雌激素对尿酸代谢和炎症反应的调节作用，使得女性对UHR的早期、持续性升高更为敏感[4,16]。这也提示在未来针对不同性别的糖尿病早期筛查中，UHR变化轨迹的应用策略应有所区别。
在亚组分析中，本研究进一步发现UHR变化轨迹与糖尿病发病风险的关联呈现人群异质性。年龄<60岁及农业户口人群中，Class 2与Class 3均显著增加糖尿病风险；非农业户口人群中仅Class 2类风险显著上升。性别分层显示，男性在Class 3类风险更高，而女性在Class 2类即显著升高。尽管交互作用未达显著水平，上述差异仍提示UHR的影响可能受人口特征调节，未来干预需考虑群体差异。同时敏感性分析结果与Cox模型结果一致，进一步支持了UHR作为糖尿病风险标志物的稳健性。
本研究是基于全国性队列数据，其结论具有较强的外推性。从公共卫生角度来看，UHR检测具有低成本、易获取的优势，可作为基层医疗机构糖尿病筛查的辅助指标；从临床实践看，持续监测UHR变化轨迹有助于识别糖尿病高危人群。
本研究仍存在一些局限：首先，未纳入餐后血糖及胰岛素水平等指标，难以全面评估胰岛素抵抗状态；其次，累积UHR计算公式采用两次检测的均值，可能忽略随访期间的波动效应；最后，本研究缺乏对血清尿酸来源（饮食或内源性合成）及HDL-C亚组分的分析，限制了机制探讨的深度。未来研究可从以下方向拓展：（1）结合代谢组学探索UHR与其他新型标志物（如内脏脂肪指数）的联合预测价值；（2）开展干预性研究，验证调节UHR水平能否延缓糖尿病进展；（3）引入基因多态性分析，揭示UHR个体差异的遗传学基础。
综上，本研究发现UHR及其动态变化轨迹与中老年人糖尿病发病风险密切相关，可作为糖尿病早期预警的潜在生物标志物。将UHR监测纳入常规健康管理，有望通过“代谢状态评估 - 高危人群识别 -早期干预”的三级防控模式降低糖尿病发病率。

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doi: 10.20043/j.cnki.MPM.202511453
  • 接收时间：2025-11-25
  • 首发时间：2026-09-28
  • 出版时间：2026-08-10
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  • 收稿日期：2025-11-25
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    中国医科大学附属盛京医院，辽宁 沈阳 110001

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黎思含，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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