Article(id=1228016568437764261, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1228016566646801206, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202504364, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1744992000000, receivedDateStr=2025-04-19, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1770711565360, onlineDateStr=2026-02-10, pubDate=1758729600000, pubDateStr=2025-09-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1770711565360, onlineIssueDateStr=2026-02-10, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1770711565360, creator=13701087609, updateTime=1770711565360, updator=13701087609, issue=Issue{id=1228016566646801206, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='18', pageStart='3265', pageEnd='3456', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1770711564932, creator=13701087609, updateTime=1770711815039, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1228017615784833769, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1228016566646801206, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1228017615784833770, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1228016566646801206, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=3437, endPage=3442, ext={EN=ArticleExt(id=1228016570031599795, articleId=1228016568437764261, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Association between metabolic syndrome-insulin resistance index and risk of kidney stones, columnId=1228016569138213037, journalTitle=Modern Preventive Medicine, columnName=Clinical Medicine and Prevention, runingTitle=null, highlight=null, articleAbstract=
Objective

To investigate the association between the metabolic syndrome-insulin resistance index (METS-IR) and the risk of kidney stones, as well as its potential threshold effect.

Methods

Data were obtained from individuals undergoing health screening at the Affiliated Hospital of Yangzhou University in 2022. This cross-sectional study included 80 046 eligible participants. Participants were divided into three tertile groups based on METS-IR levels. Multivariate logistic regression was used to analyze the association between METS-IR and kidney stone risk. Piecewise linear regression was applied to evaluate the threshold effect, with sex-stratified and multiple subgroup analyses conducted.

Results

The prevalence of kidney stones increased with higher METS-IR tertiles (low: 3.83%, medium: 6.47%, high: 9.91%). After multivariate adjustment, each standard deviation increase in METS-IR was associated with a 20% higher risk of kidney stones in women (OR=1.20, 95% CI: 1.08-1.33) and a 24% higher risk in men (OR=1.24, 95% CI: 1.18-1.31). Compared with the low METS-IR group, the high METS-IR group had a 41% higher risk in women (OR=1.41, 95% CI: 1.13-1.74) and a 56% higher risk in men (OR=1.56, 95% CI: 1.37-1.78), both showing significant dose-response relationships. A clear threshold effect was observed in men (inflection point=43.72, P<0.001), where each 1-unit increase in METS-IR below the inflection point was associated with a 5% higher risk (OR=1.05, 95% CI: 1.04-1.06), while the association weakened above the inflection point (OR=1.01, 95% CI: 1.00-1.02).

Conclusion

METS-IR is significantly associated with kidney stone risk, with a threshold effect observed in men. METS-IR may serve as a potential marker for assessing kidney stone risk, providing a new perspective for early screening and intervention, particularly in high-risk populations at the early stage of metabolic abnormalities.

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目的

探究胰岛素抵抗代谢评分(metabolic syndrome-insulin resistance index,METS-IR)与肾结石患病风险的关联及其潜在阈值效应。

方法

基于横断面研究设计,纳入80 046名符合条件的参与者。根据METS-IR水平分为三个三分位组,采用多因素logistic回归分析METS-IR与肾结石患病风险关联。应用分段线性回归分析评估阈值效应,并进行性别分层及多重亚组分析。

结果

肾结石患病率随METS-IR三分位升高而增加(低:3.83%、中:6.47%、高:9.91%)。多因素调整后,METS-IR每增加1个标准差,女性和男性肾结石风险分别增加20%(OR=1.20,95% CI:1.08~1.33)和24%(OR=1.24,95% CI:1.18~1.31)。与低METS-IR组相比,高METS-IR组女性和男性肾结石风险分别增加41%(OR=1.41,95% CI:1.13~1.74)和56%(OR=1.56,95% CI:1.37~1.78),均呈显著剂量-反应关系。男性中存在明确阈值效应(拐点=43.72,P<0.001),拐点下方METS-IR每增加1单位,风险增加5%(OR=1.05,95% CI:1.04~1.06),拐点上方关联减弱(OR=1.01,95% CI:1.00~1.02)。

结论

METS-IR与肾结石风险显著相关,且在男性中表现出阈值效应。METS-IR可作为评估肾结石风险的潜在标志物,为早期筛查和干预提供新视角,尤其适用于代谢异常早期阶段的高危人群。

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李一帆,E-mail:
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伍之岳和郑生旗为共同第一作者。

伍之岳(1984—),男,硕士在读,研究方向:泌尿系结石的诊疗;

郑生旗(1997—),男,硕士在读,研究方向:泌尿系结石的发生发展机制

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伍之岳(1984—),男,硕士在读,研究方向:泌尿系结石的诊疗;

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Proteomics, 2019, 19(19): e1900095., articleTitle=Protective cellular mechanism of estrogen against kidney stone formation: a proteomics approach and functional validation, refAbstract=null), Reference(id=1228016581242975152, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1228016568437764261, doi=null, pmid=null, pmcid=null, year=2016, volume=11, issue=10, pageStart=1834, pageEnd=1844, url=null, language=null, rfNumber=[21], rfOrder=21, authorNames=Ferraro PM, Mandel EI, Curhan GC, journalName=Clinical Journal of the American Society of Nephrology, refType=null, unstructuredReference=Ferraro PM, Mandel EI, Curhan GC, et al. Dietary protein and Potassium, Diet-Dependent net acid load, and risk of incident kidney stones[J]. 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注:A:男性人群中METS-IR与肾结石患病风险;B:女性人群中METS-IR与肾结石患病风险。图中中间的实线是拟合线,阴影部分是95%可信区间。

, figureFileSmall=2ewXG2Ksbc4emXZuR4uJSw==, figureFileBig=pgXqM8jvEY1MZV37pj3pBA==, tableContent=null), ArticleFig(id=1228016576918647396, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1228016568437764261, language=EN, label=Table 1, caption=

Baseline characteristics of the study population by METS-IR tertiles

, figureFileSmall=null, figureFileBig=null, tableContent=
变量低METS-IR组
(<32.19)
中METS-IR组
(32.19~38.91)
高METS-IR组
(>38.91)
P
人数26 67926 66426 703
年龄(岁)44.05±14.2248.73±14.2447.09±13.39<0.001
性别(n/%)<0.001
女性19 182(71.90)10 379(38.93)4 872(18.25)
男性7 497(28.10)16 285(61.07)21 831(81.75)
高血压(n/%)<0.001
22 564(84.58)17 847(66.93)13 674(51.21)
4 115(15.42)8 817(33.07)13 029(48.79)
肾结石(n/%)<0.001
25 657(96.17)24 938(93.53)24 058(90.09)
1 022(3.83)1 726(6.47)2 645(9.91)
BMI(kg/m220.77±1.7224.05±1.5327.64±2.72<0.001
FBG(mmol/L)5.04±0.465.25±0.525.42±0.58<0.001
TC(mmol/L)4.78±0.904.87±0.914.95±0.94<0.001
TG(mmol/L)0.96±0.411.48±0.692.64±2.00<0.001
HDL-C(mmol/L)1.60±0.311.29±0.231.07±0.19<0.001
LDL-C(mmol/L)2.62±0.722.86±0.732.83±0.76<0.001
SCr(μmol/L)59.91±16.8267.79±18.0572.40±20.91<0.001
SU(μmol/L)287.11±70.16344.48±80.01398.36±86.46<0.001
), ArticleFig(id=1228016577065448049, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1228016568437764261, language=CN, label=表1, caption=

METS-IR三分位分组的人群基线特征

, figureFileSmall=null, figureFileBig=null, tableContent=
变量低METS-IR组
(<32.19)
中METS-IR组
(32.19~38.91)
高METS-IR组
(>38.91)
P
人数26 67926 66426 703
年龄(岁)44.05±14.2248.73±14.2447.09±13.39<0.001
性别(n/%)<0.001
女性19 182(71.90)10 379(38.93)4 872(18.25)
男性7 497(28.10)16 285(61.07)21 831(81.75)
高血压(n/%)<0.001
22 564(84.58)17 847(66.93)13 674(51.21)
4 115(15.42)8 817(33.07)13 029(48.79)
肾结石(n/%)<0.001
25 657(96.17)24 938(93.53)24 058(90.09)
1 022(3.83)1 726(6.47)2 645(9.91)
BMI(kg/m220.77±1.7224.05±1.5327.64±2.72<0.001
FBG(mmol/L)5.04±0.465.25±0.525.42±0.58<0.001
TC(mmol/L)4.78±0.904.87±0.914.95±0.94<0.001
TG(mmol/L)0.96±0.411.48±0.692.64±2.00<0.001
HDL-C(mmol/L)1.60±0.311.29±0.231.07±0.19<0.001
LDL-C(mmol/L)2.62±0.722.86±0.732.83±0.76<0.001
SCr(μmol/L)59.91±16.8267.79±18.0572.40±20.91<0.001
SU(μmol/L)287.11±70.16344.48±80.01398.36±86.46<0.001
), ArticleFig(id=1228016577199665791, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1228016568437764261, language=EN, label=Table 2, caption=

Association between METS-IR and kidney stone risk

, figureFileSmall=null, figureFileBig=null, tableContent=
变量模型1模型2模型3
OR值(95% CIPOR值(95% CIPOR值(95% CIP
女性
METS-IR(每标准差)1.26(1.18~1.35)<0.0011.22(1.13~1.31)<0.0011.20(1.08~1.33)<0.001
METS-IR三分位
1.001.001.00
1.28(1.12~1.46)<0.0011.21(1.05~1.39)0.0071.15(0.99~1.35)0.075
1.65(1.41~1.94)<0.0011.52(1.28~1.80)<0.0011.41(1.13~1.74)0.002
P趋势<0.001<0.001<0.001
男性
METS-IR(每标准差)1.27(1.23~1.31)<0.0011.26(1.22~1.30)<0.0011.24(1.18~1.31)<0.001
METS-IR三分位
1.001.001.00
1.35(1.21~1.51)<0.0011.31(1.18~1.46)<0.0011.23(1.10~1.39)<0.001
1.87(1.68~2.07)<0.0011.77(1.59~1.97)<0.0011.56(1.37~1.78)<0.001
P趋势<0.001<0.001<0.001
), ArticleFig(id=1228016577321300619, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1228016568437764261, language=CN, label=表2, caption=

METS-IR与肾结石患病风险

, figureFileSmall=null, figureFileBig=null, tableContent=
变量模型1模型2模型3
OR值(95% CIPOR值(95% CIPOR值(95% CIP
女性
METS-IR(每标准差)1.26(1.18~1.35)<0.0011.22(1.13~1.31)<0.0011.20(1.08~1.33)<0.001
METS-IR三分位
1.001.001.00
1.28(1.12~1.46)<0.0011.21(1.05~1.39)0.0071.15(0.99~1.35)0.075
1.65(1.41~1.94)<0.0011.52(1.28~1.80)<0.0011.41(1.13~1.74)0.002
P趋势<0.001<0.001<0.001
男性
METS-IR(每标准差)1.27(1.23~1.31)<0.0011.26(1.22~1.30)<0.0011.24(1.18~1.31)<0.001
METS-IR三分位
1.001.001.00
1.35(1.21~1.51)<0.0011.31(1.18~1.46)<0.0011.23(1.10~1.39)<0.001
1.87(1.68~2.07)<0.0011.77(1.59~1.97)<0.0011.56(1.37~1.78)<0.001
P趋势<0.001<0.001<0.001
), ArticleFig(id=1228016577438741140, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1228016568437764261, language=EN, label=Table 3, caption=

Subgroup analysis of the female population

, figureFileSmall=null, figureFileBig=null, tableContent=
分组OR值(95% CIPP交互作用
年龄(岁)0.846
<401.27(1.06~1.52)0.011
40~<601.16(1.00~1.35)0.057
≥601.10(0.86~1.42)0.451
高血压0.885
1.21(1.07~1.38)0.003
1.13(0.94~1.35)0.190
TG(mmol/L)0.885
<1.71.25(1.10~1.42)<0.001
≥1.71.07(0.89~1.28)0.469
HDL-C(mmol/L)0.806
<1.01.30(0.94~1.80)0.116
≥1.01.19(1.08~1.32)<0.001
LDL-C(mmol/L)0.883
<3.41.27(1.13~1.42)<0.001
≥3.40.97(0.76~1.24)0.788
UA(μmol/L)0.007
<3601.26(1.12~1.42)<0.001
≥3601.06(0.85~1.32)0.611
), ArticleFig(id=1228016577560375966, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1228016568437764261, language=CN, label=表3, caption=

女性人群的亚组分析

, figureFileSmall=null, figureFileBig=null, tableContent=
分组OR值(95% CIPP交互作用
年龄(岁)0.846
<401.27(1.06~1.52)0.011
40~<601.16(1.00~1.35)0.057
≥601.10(0.86~1.42)0.451
高血压0.885
1.21(1.07~1.38)0.003
1.13(0.94~1.35)0.190
TG(mmol/L)0.885
<1.71.25(1.10~1.42)<0.001
≥1.71.07(0.89~1.28)0.469
HDL-C(mmol/L)0.806
<1.01.30(0.94~1.80)0.116
≥1.01.19(1.08~1.32)<0.001
LDL-C(mmol/L)0.883
<3.41.27(1.13~1.42)<0.001
≥3.40.97(0.76~1.24)0.788
UA(μmol/L)0.007
<3601.26(1.12~1.42)<0.001
≥3601.06(0.85~1.32)0.611
), ArticleFig(id=1228016577694593707, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1228016568437764261, language=EN, label=Table 4, caption=

Subgroup analysis of the male population

, figureFileSmall=null, figureFileBig=null, tableContent=
分组OR值(95% CIPP交互作用
年龄(岁)0.225
<401.28(1.18~1.39)<0.001
40~<601.18(1.10~1.27)<0.001
≥601.27(1.11~1.46)<0.001
高血压0.672
1.27(1.18~1.37)<0.001
1.19(1.11~1.27)<0.001
TG(mmol/L)<0.001
<1.71.37(1.27~1.48)<0.001
≥1.71.14(1.07~1.22)<0.001
HDL-C(mmol/L)0.039
<1.01.16(1.07~1.26)<0.001
≥1.01.25(1.18~1.32)<0.001
LDL-C(mmol/L)0.501
<3.41.25(1.18~1.32)<0.001
≥3.41.24(1.11~1.39)<0.001
UA(μmol/L)0.086
<4201.33(1.24~1.42)<0.001
≥4201.17(1.08~1.26)<0.001
), ArticleFig(id=1228016577853977267, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1228016568437764261, language=CN, label=表4, caption=

男性人群的亚组分析

, figureFileSmall=null, figureFileBig=null, tableContent=
分组OR值(95% CIPP交互作用
年龄(岁)0.225
<401.28(1.18~1.39)<0.001
40~<601.18(1.10~1.27)<0.001
≥601.27(1.11~1.46)<0.001
高血压0.672
1.27(1.18~1.37)<0.001
1.19(1.11~1.27)<0.001
TG(mmol/L)<0.001
<1.71.37(1.27~1.48)<0.001
≥1.71.14(1.07~1.22)<0.001
HDL-C(mmol/L)0.039
<1.01.16(1.07~1.26)<0.001
≥1.01.25(1.18~1.32)<0.001
LDL-C(mmol/L)0.501
<3.41.25(1.18~1.32)<0.001
≥3.41.24(1.11~1.39)<0.001
UA(μmol/L)0.086
<4201.33(1.24~1.42)<0.001
≥4201.17(1.08~1.26)<0.001
), ArticleFig(id=1228016577975612092, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1228016568437764261, language=EN, label=Table 5, caption=

Threshold effect analysis of the association between METS-IR and kidney stone risk

, figureFileSmall=null, figureFileBig=null, tableContent=
METS-IRULR检验PLR检验LRT
OR值(95% CIPOR值(95% CIPP
女性<0.0010.395
<31.831.02 (1.01~1.04)1.04 (1.01~1.07)0.022
≥31.831.02 (1.00~1.04)0.016
男性<0.001<0.001
<43.721.03 (1.02~1.04)1.05 (1.04~1.06)<0.001
≥43.721.01 (1.00~1.02)0.057
), ArticleFig(id=1228016578160161484, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1228016568437764261, language=CN, label=表5, caption=

METS-IR与肾结石风险的阈值效应分析

, figureFileSmall=null, figureFileBig=null, tableContent=
METS-IRULR检验PLR检验LRT
OR值(95% CIPOR值(95% CIPP
女性<0.0010.395
<31.831.02 (1.01~1.04)1.04 (1.01~1.07)0.022
≥31.831.02 (1.00~1.04)0.016
男性<0.001<0.001
<43.721.03 (1.02~1.04)1.05 (1.04~1.06)<0.001
≥43.721.01 (1.00~1.02)0.057
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胰岛素抵抗代谢评分与肾结石患病风险的关联性分析
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伍之岳 1, 2 , 郑生旗 1 , 蒋小华 3 , 王佳龙 1 , 郭行洲 1 , 李一帆 1
现代预防医学 | 临床与预防 2025,52(18): 3437-3442
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现代预防医学 | 临床与预防 2025, 52(18): 3437-3442
胰岛素抵抗代谢评分与肾结石患病风险的关联性分析
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伍之岳1, 2, 郑生旗1, 蒋小华3, 王佳龙1, 郭行洲1, 李一帆1
作者信息
  • 1.扬州大学附属医院泌尿外科,江苏 扬州 225001
  • 2.扬中市人民医院泌尿外科
  • 3.宜兴市第二人民医院手术室
  • 伍之岳(1984—),男,硕士在读,研究方向:泌尿系结石的诊疗;

    郑生旗(1997—),男,硕士在读,研究方向:泌尿系结石的发生发展机制

通讯作者:

李一帆,E-mail:
Association between metabolic syndrome-insulin resistance index and risk of kidney stones
Zhi-yue WU1, 2, Sheng-qi ZHENG1, Xiao-hua JIANG3, Jia-long WANG1, Xing-zhou GUO1, Yi-fan LI1
Affiliations
  • Department of Urology, Affiliated Hospital of Yangzhou University, Yangzhou, Jiangsu 225001, China
出版时间: 2025-09-25 doi: 10.20043/j.cnki.MPM.202504364
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目的

探究胰岛素抵抗代谢评分(metabolic syndrome-insulin resistance index,METS-IR)与肾结石患病风险的关联及其潜在阈值效应。

方法

基于横断面研究设计,纳入80 046名符合条件的参与者。根据METS-IR水平分为三个三分位组,采用多因素logistic回归分析METS-IR与肾结石患病风险关联。应用分段线性回归分析评估阈值效应,并进行性别分层及多重亚组分析。

结果

肾结石患病率随METS-IR三分位升高而增加(低:3.83%、中:6.47%、高:9.91%)。多因素调整后,METS-IR每增加1个标准差,女性和男性肾结石风险分别增加20%(OR=1.20,95% CI:1.08~1.33)和24%(OR=1.24,95% CI:1.18~1.31)。与低METS-IR组相比,高METS-IR组女性和男性肾结石风险分别增加41%(OR=1.41,95% CI:1.13~1.74)和56%(OR=1.56,95% CI:1.37~1.78),均呈显著剂量-反应关系。男性中存在明确阈值效应(拐点=43.72,P<0.001),拐点下方METS-IR每增加1单位,风险增加5%(OR=1.05,95% CI:1.04~1.06),拐点上方关联减弱(OR=1.01,95% CI:1.00~1.02)。

结论

METS-IR与肾结石风险显著相关,且在男性中表现出阈值效应。METS-IR可作为评估肾结石风险的潜在标志物,为早期筛查和干预提供新视角,尤其适用于代谢异常早期阶段的高危人群。

肾结石  /  胰岛素抵抗代谢评分  /  代谢综合征  /  胰岛素抵抗
Objective

To investigate the association between the metabolic syndrome-insulin resistance index (METS-IR) and the risk of kidney stones, as well as its potential threshold effect.

Methods

Data were obtained from individuals undergoing health screening at the Affiliated Hospital of Yangzhou University in 2022. This cross-sectional study included 80 046 eligible participants. Participants were divided into three tertile groups based on METS-IR levels. Multivariate logistic regression was used to analyze the association between METS-IR and kidney stone risk. Piecewise linear regression was applied to evaluate the threshold effect, with sex-stratified and multiple subgroup analyses conducted.

Results

The prevalence of kidney stones increased with higher METS-IR tertiles (low: 3.83%, medium: 6.47%, high: 9.91%). After multivariate adjustment, each standard deviation increase in METS-IR was associated with a 20% higher risk of kidney stones in women (OR=1.20, 95% CI: 1.08-1.33) and a 24% higher risk in men (OR=1.24, 95% CI: 1.18-1.31). Compared with the low METS-IR group, the high METS-IR group had a 41% higher risk in women (OR=1.41, 95% CI: 1.13-1.74) and a 56% higher risk in men (OR=1.56, 95% CI: 1.37-1.78), both showing significant dose-response relationships. A clear threshold effect was observed in men (inflection point=43.72, P<0.001), where each 1-unit increase in METS-IR below the inflection point was associated with a 5% higher risk (OR=1.05, 95% CI: 1.04-1.06), while the association weakened above the inflection point (OR=1.01, 95% CI: 1.00-1.02).

Conclusion

METS-IR is significantly associated with kidney stone risk, with a threshold effect observed in men. METS-IR may serve as a potential marker for assessing kidney stone risk, providing a new perspective for early screening and intervention, particularly in high-risk populations at the early stage of metabolic abnormalities.

Kidney stone  /  Metabolic syndrome-insulin resistance index  /  Metabolic syndrome  /  Insulin resistance
伍之岳, 郑生旗, 蒋小华, 王佳龙, 郭行洲, 李一帆. 胰岛素抵抗代谢评分与肾结石患病风险的关联性分析. 现代预防医学, 2025 , 52 (18) : 3437 -3442 . DOI: 10.20043/j.cnki.MPM.202504364
Zhi-yue WU, Sheng-qi ZHENG, Xiao-hua JIANG, Jia-long WANG, Xing-zhou GUO, Yi-fan LI. Association between metabolic syndrome-insulin resistance index and risk of kidney stones[J]. Modern Preventive Medicine, 2025 , 52 (18) : 3437 -3442 . DOI: 10.20043/j.cnki.MPM.202504364
肾结石作为全球公共卫生的重要负担,其患病率与经济成本持续上升[1-2]。在中国,一项基于22个省份46项研究的荟萃分析显示约7.8%的成年人受到肾结石影响[3]。近年来,越来越多的证据表明代谢性疾病如肥胖、糖尿病和代谢综合征与肾结石形成密切相关[4]。胰岛素抵抗(insulin resistance, IR)作为这些代谢性疾病的共同病理生理特征,被认为是肾结石发生发展的重要危险因素[5]。IR通过降低尿液pH值、减少柠檬酸排泄及增加尿钙排泄等机制促进结石形成[6]。高胰岛素血症-正糖钳夹技术被公认为评估胰岛素抵抗的金标准,但其操作复杂、耗时且成本高昂,限制了其在大规模流行病学研究和日常临床实践中的应用[7]。胰岛素抵抗代谢评分(metabolic syndrome-insulin resistance index, METS-IR)作为一种新型胰岛素抵抗评估工具,结合了脂代谢、糖代谢和肥胖指标,突破传统胰岛素抵抗检测的临床转化瓶颈,实现基于常规体检数据的精准代谢评估[8]。本研究基于大规模队列,系统揭示METS-IR与肾结石风险的关系,为代谢相关性肾结石的早期识别提供循证依据。
本研究采用横断面研究设计,数据源自2022自然年度在扬州大学附属医院健康管理中心健康筛查的人群资料(图1)。纳入标准为在该中心完成肾脏超声检查且年龄≥18岁的健康筛查者(n=96 820)。排除标准包括存在肾脏手术治疗史个体(n=53)、代谢指标检测缺失者(n=8 881)以及合并糖尿病或接受降糖治疗者(n=7 840)。最终纳入80 046名受检者。
数据采集工作由健康管理中心专业人员执行,记录包括年龄、性别、基础疾病史在内的一般人口学特征。高血压诊断标准为收缩压≥140 mmHg和/或舒张压≥90 mmHg、既往诊断记录或接受减血压治疗。测量身高和体质量,并计算体质量指数(body mass index,BMI)。所有受检者在采血前禁食12 h,收集所有样本并统一使用全自动生化分析仪进行检测。测定实验室指标包括空腹血糖(fasting glucose, FBG)、总胆固醇(total cholesterol, TC)、甘油三酯(triglycerides, TG)、高密度脂蛋白胆固醇(high-density lipoprotein cholesterol, HDL-C)、低密度脂蛋白胆固醇(low-density lipoprotein cholesterol, LDL-C)、血清肌酐(Serum creatinine, Cr)、血清尿酸(serum uric acid, SU)。METS-IR的计算公式如下:
由执业超声医师进行肾结石的超声检查。采用GE LOGIQ E9多普勒超声诊断系统,配置腹部凸阵探头,扫描频率为3.5-5.0 MHz。肾结石超声诊断标准基于经多切面(冠状位及矢状位)扫查证实肾解剖区域内存在局灶性强回声灶,并伴特征性后场声影,且排除血管钙化伪影。
采用R语言软件(版本4.2.0)进行统计分析。基线资料比较中分类变量应用卡方检验,连续变量中正态分布数据采用单因素方差分析,偏态分布数据选用Kruskal-Wallis H检验。按三分位数法,将总研究人群 METS-IR 水平分为三组:低组(<33.3%分位数)、中组(33.3%~66.7%分位数)、高组(>66.7%分位数)。运用多因素 logistic 回归模型,探讨METS-IR与肾结石的关联性。模型构建遵循多重共线性诊断准则,方差膨胀因子<10且与肾结石显著相关的协变量纳入最终模型。模型1为未校正模型。模型2校正年龄、高血压和BMI。模型3进一步校正代谢相关指标(TG、HDL-C、LDL-C)及肾功能指标(SCr、SU)。采用带平滑曲线拟合的分段线性回归模型探究METS-IR与肾结石风险的非线性剂量-反应关系。通过递归算法确定非线性关系的折点,并据此构建分段模型进行分析。对数似然比检验用于比较包含非线性项的模型与传统线性模型的拟合优度。亚组分析按年龄、高血压和血脂异常进行分层建模,交互作用检验采用Wald检验。检验水准α=0.05。
本研究严格遵循《赫尔辛基宣言》伦理准则及国家《涉及人的健康研究伦理审查办法》要求,研究方案经扬州大学附属医院伦理委员会审核并批准(伦理批件号:2023-YKL01-课13)。本研究属回顾性研究设计,原始数据来源于历史健康筛查数据库,且所有健康信息均经过匿名化脱敏处理,伦理委员会豁免知情同意书的要求。
本研究共纳入80 046名参与者,根据METS-IR值分为三个三分位组(低:<32.19,中:32.19~38.91,高:>38.91)(表1)。高METS-IR组的代谢异常及疾病风险显著高于低、中组。相较于低组(年龄44.05±14.22岁,女性71.90%),高组人群年龄更高(47.09±13.39岁,P<0.001),男性占比显著增加(81.75% vs 28.10%)。肾结石患病率表现出显著递增趋势(低:3.83%,中:6.47%,高:9.91%,P<0.001)。高血压患病率同样呈梯度上升(低:15.42%,中:33.07%,高:48.79%,P<0.001),代谢指标方面,各组间BMI、FBG、TC及TG水平均随METS-IR三分位升高而增加,而HDL-C水平则呈现递减趋势(P<0.001)。
考虑到METS-IR各三分位组间男性比例存在显著差异及男女在肾结石患病率和代谢特征方面的固有差异,我们对METS-IR与肾结石关联进行了性别分层分析。多因素logistic回归分析表明,METS-IR与肾结石风险呈显著正相关,且存在性别差异(表2)。在完全调整模型(模型3)中,每增加1个标准差(SD)的METS-IR,女性患病风险升高20%(OR=1.20, 95% CI:1.08~1.33, P<0.001),男性风险升高24%(OR=1.24, 95% CI:1.18~1.31, P<0.001)。按三分位分层后,与低METS-IR组相比,高METS-IR组的女性肾结石风险增加41%(OR=1.41,95% CI:1.13~1.74,P=0.002),男性中、高METS-IR组肾结石风险分别增加23%(OR=1.23,95% CI:1.10~1.39,P<0.001)和56%(OR=1.56,95% CI:1.37~1.78,P<0.001),均呈现显著剂量-反应关系(P趋势<0.001)。
亚组分析显示,METS-IR与肾结石正相关在不同亚组中普遍存在,但表现出亚组异质性。在女性人群中(表3),METS-IR对肾结石的影响受尿酸水平显著修饰(P交互作用=0.007),当SU<360 μmol/L时,风险增加26%(OR=1.26, 95% CI:1.12~1.42, P<0.001),而在SU≥360 μmol/L时关联消失(OR=1.06,95% CI:0.85~1.32,P=0.611)。此外,年龄≥60岁女性未显示显著风险(OR=1.10,95% CI:0.86~1.42,P=0.451)。
男性人群中(表4),METS-IR与肾结石关联在所有亚组中均保持显著性。TG(P交互作用<0.001)和HDL-C(P交互作用=0.039)与METS-IR存在显著交互作用,关联强度在TG<1.7 mmol/L(OR=1.37,95% CI:1.27~1.48,P<0.001)和HDL-C≥1.0 mmol/L(OR=1.25,95% CI:1.18~1.32,P<0.001)亚组中更强。
分段回归分析揭示性别特异性非线性关联(表5)。男性人群在METS-IR=43.72处存在显著拐点(PLRT<0.001),低于该阈值时,每单位METS-IR增加导致肾结石患病风险上升5%(OR=1.05, 95% CI:1.04~1.06),超过阈值后关联无统计学意义(OR=1.01, 95% CI:1.00~1.02, P=0.057)(图2A)。女性人群中虽观察到拐点,但对数似然比检验未达显著性(P=0.395),表明METS-IR与肾结石风险在女性人群中呈线性关系(图2B)。
本研究首次在中国人群中系统评估了METS-IR与肾结石风险的关联性。研究结果表明,METS-IR 与肾结石发病风险呈显著正相关,且存在性别差异及非线性剂量反应关系。这一发现与既往关于胰岛素抵抗与肾结石关系的研究结果一致。Shen等[9]在美国人群中的研究发现,METS-IR每增加一个单位,肾结石发生风险增加1.23%。另一项以甘油三酯-葡萄糖指数(triglyceride-glucose index, TyG)作为胰岛素抵抗评价指标的研究则表明,在美国人群中TyG 每升高1个单位,肾结石患病风险及复发风险分别升高12%和26%[10]。但上述两项研究均依赖受试者自我报告问卷进行肾结石诊断。国内基于2 490例体检人群的观察性研究采用超声检查证实,TyG指数越高肾结石患病风险越大,并存在明显的线性关系[11]
胰岛素抵抗影响肾结石形成的机制已有多项研究阐明。Cupisti等[12]报道了钙性肾结石患者中胰岛素抵抗与尿柠檬酸排泄降低的关系,提示胰岛素抵抗可能通过影响尿液成分促进结石形成。近年来的研究发现,胰岛素抵抗可通过抑制肾脏氨生成并增加钠和碳酸氢盐重吸收,导致尿液pH值降低[13]。尿液pH值低时,尿酸盐转化为不溶性尿酸的比例增加,促进尿酸结石形成[14]。此外,胰岛素抵抗还可增加肾小管对柠檬酸的吸收并降低尿柠檬酸水平,而柠檬酸是钙结晶的重要抑制剂,其减少会增加钙结石风险[15]
本研究中高METS-IR组TG显著升高而HDL-C水平明显降低,这一特征与代谢综合征的脂质代谢紊乱一致。脂质代谢异常可能通过多种途径影响肾结石形成,高甘油三酯血症与氧化应激和炎症状态相关,可促进肾小管局部炎症和结晶体形成,低HDL-C失去其抗炎和抗氧化保护作用,增加肾组织损伤风险[16-17]。亚组分析中,男性人群低TG(<1.7 mmol/L)和高HDL-C(≥1.0 mmol/L)人群METS-IR与肾结石的关联反而更强,提示 METS-IR 可能在脂质代谢紊乱尚未显性化时,即可通过早期胰岛素抵抗状态预测结石风险。
观察到METS-IR与肾结石关系存在明显性别差异,男性不仅肾结石总体患病率高于女性,METS-IR对肾结石风险的影响在男性中也更为显著。这一性别差异与既往研究一致。流行病学研究普遍报道男性肾结石患病率高于女性[18]。基于美国人群的研究也发现METS-IR与肾结石的关联在男性中更强[19]。导致这一性别差异的原因可能多方面。首先,雌激素具有保护作用,能增加尿液柠檬酸排泄并抑制草酸钙结晶形成[20]。其次,男性通常具有更高的肉类摄入和尿酸水平,这增加了结石风险[21]。另外,男性代谢性疾病的流行率普遍高于女性,增加了胰岛素抵抗相关性肾结石的风险[18]
通过分段回归分析在METS-IR与肾结石关系中识别出性别特异性阈值点。在男性中,METS-IR=43.72处存在显著拐点,低于该阈值时风险随METS-IR快速增加,超过阈值后增速显著减缓。与男性不同,女性未检测到明显阈值效应,但研究结果仍支持在女性中监测METS-IR作为肾结石风险评估的参考指标。
本研究仍存在一些局限性:首先,横断面研究设计无法确立因果关系,未来需进行前瞻性队列研究;其次,肾结石诊断基于超声诊断,可能存在一定误差;第三,缺乏对肾结石类型的详细分类,无法区分不同类型结石与METS-IR的特异性关联;第四,未能获取详细的饮食和生活方式数据,这些因素可能是胰岛素抵抗与肾结石关系的重要调节因素。
综上,本研究首次在中国人群中证实METS-IR与肾结石风险的性别特异性非线性关联,揭示了胰岛素抵抗在结石发生中的早期预测价值。鉴于METS-IR 可通过简单临床指标快速计算,建议将其纳入肾结石高危人群的筛查体系。未来研究需通过纵向队列明确因果关系,并探索针对胰岛素抵抗的干预措施对结石预防的效果,为代谢相关肾结石管理提供新靶点。
  • 国家自然科学基金(82002675)
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2025年第52卷第18期
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doi: 10.20043/j.cnki.MPM.202504364
  • 接收时间:2025-04-19
  • 首发时间:2026-02-10
  • 出版时间:2025-09-25
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  • 收稿日期:2025-04-19
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国家自然科学基金(82002675)
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    1.扬州大学附属医院泌尿外科,江苏 扬州 225001
    2.扬中市人民医院泌尿外科
    3.宜兴市第二人民医院手术室

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李一帆,E-mail:
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

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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