Article(id=1324078073439715431, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324078037817504735, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202508337, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1755619200000, receivedDateStr=2025-08-20, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793614412888, onlineDateStr=2026-11-02, pubDate=1770652800000, pubDateStr=2026-02-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793614412888, onlineIssueDateStr=2026-11-02, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793614412888, creator=13701087609, updateTime=1793614412888, updator=13701087609, issue=Issue{id=1324078037817504735, tenantId=1146029695717560320, journalId=1227665162245664772, year='2026', volume='53', issue='3', pageStart='385', pageEnd='576', issueExtLink='null', onlineDate='null', pubDate='1770652800000', pubDateStr='2026-02-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1793614404396, creator='13701087609', updateTime=1793614474144, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1324078330420527919, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324078037817504735, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1324078330420527920, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324078037817504735, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=399, endPage=404, ext={EN=ArticleExt(id=1324078073662013544, articleId=1324078073439715431, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Association between the atherogenic index of plasma and kidney stone risk in a health examination population, columnId=1240413921954295836, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods, runingTitle=null, highlight=null, articleAbstract=
Objective

To investigate the association between the atherogenic index of plasma(AIP) and the risk of kidney stones in the Chinese population undergoing health examinations, providing evidence-based support for risk stratification and precise prevention and control.

Methods

Using cross-sectional data from 88 001 healthy individuals who underwent health examinations at Yangzhou University Affiliated Hospital in 2022, we analyzed the association between AIP and the risk of kidney stones using a multivariable logistic regression model. We also explored the nonlinear relationship using restricted cubic splines and threshold saturation effect models. The robustness of the results was assessed through sensitivity analysis, subgroup analysis, and E-value assessment.

Results

A total of 88 001 participants with complete data were included in this study. The overall prevalence of kidney stones was 6.82%, and the prevalence of kidney stones increased with increasing AIP (P<0.001).Multivariable logistic regression analysis showed that, after adjusting for relevant confounding factors, the highest AIP group had 29% higher odds of kidney stones than the lowest AIP group (OR=1.29; 95% CI: 1.17-1.42; P<0.001). AIP exhibited a nonlinear association with kidney stone risk, with a significant increase in risk when AIP<0.27 (OR=1.89; 95% CI: 1.62-2.19; P<0.001), while the trend of increased risk slowed when AIP≥0.27 (OR=1.23; 95% CI: 1.10-1.37; P<0.001). Stratified analysis revealed that the association between AIP and the risk of kidney stones was more pronounced in subgroups of participants aged ≥60 years, men, and those with diabetes and hypertension. Sensitivity analyses and interaction tests indicated that the positive correlation between AIP and kidney stone risk was stable and consistent across the population (all P>0.05).

Conclusion

This study suggests that AIP is associated with an increased risk of kidney stones. AIP may serve as a practical biomarker for assessing kidney stone risk, particularly guiding early interventions such as lipid control and improving insulin resistance. Future prospective studies are needed to validate its causal mechanisms and threshold intervention effects.

, authors=Jing-ming XU1, Wei SHA2, Yi-fan LI1, authorsList=Jing-ming XU, Wei SHA, Yi-fan LI, authorCompany=null, correspAuthors=Wei SHA, Yi-fan 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=1324078074572177519, articleId=1324078073439715431, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=健康体检人群血浆致动脉硬化指数与肾结石风险的相关性分析, 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

探讨中国健康体检人群中血浆致动脉硬化指数(atherogenic index of plasma,AIP)与肾结石患病风险之间的关联,为风险分层及精准防控提供循证依据。

方法

基于2022年扬州大学附属医院88 001名健康体检者的横断面数据,通过多变量逻辑回归模型分析AIP与肾结石患病风险的关联性,并采用限制性立方样条和阈值饱和效应模型探索非线性关系。通过敏感性分析、亚组分析和E值评估结果的稳健性。

结果

本研究共纳入了88 001名数据完整的参与者。肾结石的总患病率为 6.82%,随着AIP的增加,肾结石的患病率呈上升趋势(P<0.001)。多变量逻辑回归分析结果显示,在调整了相关混杂因素后,最高AIP组与最低AIP组的人群相比,肾结石的风险增加了0.29倍(OR=1.29,95% CI:1.17~1.42,P<0.001)。AIP与肾结石风险呈非线性关联,当AIP<0.27时风险显著增加(OR=1.89,95% CI:1.62~2.19,P<0.001),而AIP≥0.27时风险增加趋势减缓(OR=1.23,95% CI:1.10~1.37,P<0.001)。分层分析显示,AIP与肾结石患病风险之间的相关性在年龄≥60岁、男性、糖尿病和高血压的参与者亚组中更明显。敏感性分析和交互作用检验表明,人群中AIP与肾结石风险的正相关是稳定和一致的(P均 >0.05)。

结论

这项研究表明,AIP与肾结石患病风险增加有关。AIP可作为评估肾结石风险的实用生物标志物,尤其对早期干预(如控制血脂、改善胰岛素抵抗)具有指导价值。未来需前瞻性研究验证其因果机制及阈值干预效果。

, authors=许静鸣1, 沙维2, 李一帆1, authorsList=许静鸣, 沙维, 李一帆, authorCompany=null, correspAuthors=沙维, 李一帆, authorNote=

许静鸣(1986—),女,本科,主管护师,研究方向:泌尿系结石诊治及结石代谢的基础和转化研究

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沙维,E-mail:
李一帆,E-mail:
沙维与李一帆为共同通信作者
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许静鸣(1986—),女,本科,主管护师,研究方向:泌尿系结石诊治及结石代谢的基础和转化研究

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许静鸣(1986—),女,本科,主管护师,研究方向:泌尿系结石诊治及结石代谢的基础和转化研究

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Nutrients, 2023, 15(4): 877., articleTitle=Kidney stone prevention: is there a role for complementary and alternative medicine?, refAbstract=null)], funds=[Fund(id=1324078079353684120, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078073439715431, awardId=82002675, language=CN, fundingSource=国家自然科学基金(82002675), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1324078074823835760, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078073439715431, xref=1., ext=[AuthorCompanyExt(id=1324078074836418673, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078073439715431, companyId=1324078074823835760, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Department of Urology, Affiliated Hospital of Yangzhou University, Yangzhou, Jiangsu 225000, China), AuthorCompanyExt(id=1324078074849001586, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078073439715431, companyId=1324078074823835760, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.扬州大学附属医院泌尿外科,江苏 扬州 225000)]), AuthorCompany(id=1324078074916110451, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078073439715431, xref=2., ext=[AuthorCompanyExt(id=1324078074924499060, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078073439715431, companyId=1324078074916110451, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.扬州大学医学院盱眙临床学院泌尿外科,江苏 盱眙 211700)])], figs=[ArticleFig(id=1324078078099587212, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078073439715431, language=EN, label=Figure 1, caption=Nonlinear relationship between AIP and kidney stone risk, figureFileSmall=8L3u3G5/YJ1mMuC/FPt1fg==, figureFileBig=xklY+o2B4ORAWhmErE4vkA==, tableContent=null), ArticleFig(id=1324078078183473293, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078073439715431, language=CN, label=图1, caption=AIP与肾结石患病风险的非线性关系

注:根据性别、年龄、BMI、糖尿病、高血压、总胆固醇、LDL-C、尿酸、空腹血糖、总蛋白、血清肌酐和eGFR进行调整。

, figureFileSmall=8L3u3G5/YJ1mMuC/FPt1fg==, figureFileBig=xklY+o2B4ORAWhmErE4vkA==, tableContent=null), ArticleFig(id=1324078078380605582, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078073439715431, language=EN, label=Table 1, caption=

Baseline characteristics of the examination cohort [(),n(%)]

, figureFileSmall=null, figureFileBig=null, tableContent=
变量AIP分组统计量P值
Q1(n=22 000)Q2(n=22 001)Q3(n=22 000)Q4 (n=22 000)
年龄(岁)43.2±13.447.3±14.048.9±13.648.5±12.5803.5<0.001
性别14 476.8<0.001
男6 175 (28.1)10 948 (49.8)14 975 (68.1)17 939 (81.5)
女15 825 (71.9)11 053 (50.2)7 025 (31.9)4 061 (18.4)
肾结石718.8<0.001
否21 140 (96.1)20 752 (94.3)20 312 (92.3)19 795 (90.0)
是860 (3.9)1 249 (5.7)1 688 (7.7)2 205 (10.0)
糖尿病2 145.9<0.001
否21 537 (97.9)20 671 (94.0)19 914 (90.5)18 825 (85.6)
是643 (2.1)1 330 (6.0)2 086 (9.5)3 175 (14.4)
高血压5 735.6<0.001
否18 636 (84.7)15 825 (71.9)13 584 (61.7)11 580 (52.6)
是3 364 (15.3)6 175 (28.1)8 416 (38.3)10 420 (47.4)
BMI( kg/m2)21.9±2.723.7±3.125.1±3.226.2±3.27 772.7<0.001
血清肌酐( μmol/L)59.9±15.965.0±20.968.9±19.272.0±23.41 464.5<0.001
总蛋白( g/L)73.0±4.273.5±4.274.0±4.274.9±4.2752.7<0.001
总胆固醇( mmol/L)4.7±0.94.8±0.95.0±0.95.1±0.9768.9<0.001
HDL-C( mmol/L)1.7±0.31.4±0.21.2±0.21.0±0.230 079.7<0.001
LDL-C( mmol/L)2.5±0.72.8±0.73.0±0.72.8±0.81 503.6<0.001
甘油三酯( mmol/L)0.7±0.21.1±0.21.7±0.33.1±1.257 314.5<0.001
尿酸( μmol/L)286.9±71.4325.7±79.9359.2±84.5395.2±88.56 974.6<0.001
空腹血糖( mmol/L)5.1±0.85.4±1.05.6±1.35.9±1.71 441.6<0.001
eGFR[ml/(min·1.73m2)]95.4±15.692.5±16.390.4±18.287.9±15.11 071.4<0.001
), ArticleFig(id=1324078078489657487, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078073439715431, language=CN, label=表1, caption=

体检人群的基线资料 [(),n(%)]

, figureFileSmall=null, figureFileBig=null, tableContent=
变量AIP分组统计量P值
Q1(n=22 000)Q2(n=22 001)Q3(n=22 000)Q4 (n=22 000)
年龄(岁)43.2±13.447.3±14.048.9±13.648.5±12.5803.5<0.001
性别14 476.8<0.001
男6 175 (28.1)10 948 (49.8)14 975 (68.1)17 939 (81.5)
女15 825 (71.9)11 053 (50.2)7 025 (31.9)4 061 (18.4)
肾结石718.8<0.001
否21 140 (96.1)20 752 (94.3)20 312 (92.3)19 795 (90.0)
是860 (3.9)1 249 (5.7)1 688 (7.7)2 205 (10.0)
糖尿病2 145.9<0.001
否21 537 (97.9)20 671 (94.0)19 914 (90.5)18 825 (85.6)
是643 (2.1)1 330 (6.0)2 086 (9.5)3 175 (14.4)
高血压5 735.6<0.001
否18 636 (84.7)15 825 (71.9)13 584 (61.7)11 580 (52.6)
是3 364 (15.3)6 175 (28.1)8 416 (38.3)10 420 (47.4)
BMI( kg/m2)21.9±2.723.7±3.125.1±3.226.2±3.27 772.7<0.001
血清肌酐( μmol/L)59.9±15.965.0±20.968.9±19.272.0±23.41 464.5<0.001
总蛋白( g/L)73.0±4.273.5±4.274.0±4.274.9±4.2752.7<0.001
总胆固醇( mmol/L)4.7±0.94.8±0.95.0±0.95.1±0.9768.9<0.001
HDL-C( mmol/L)1.7±0.31.4±0.21.2±0.21.0±0.230 079.7<0.001
LDL-C( mmol/L)2.5±0.72.8±0.73.0±0.72.8±0.81 503.6<0.001
甘油三酯( mmol/L)0.7±0.21.1±0.21.7±0.33.1±1.257 314.5<0.001
尿酸( μmol/L)286.9±71.4325.7±79.9359.2±84.5395.2±88.56 974.6<0.001
空腹血糖( mmol/L)5.1±0.85.4±1.05.6±1.35.9±1.71 441.6<0.001
eGFR[ml/(min·1.73m2)]95.4±15.692.5±16.390.4±18.287.9±15.11 071.4<0.001
), ArticleFig(id=1324078078590320784, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078073439715431, language=EN, label=Table 2, caption=

Multivariable logistic regression analysis of AIP and kidney stone risk

, figureFileSmall=null, figureFileBig=null, tableContent=
指标未调整模型模型 I模型 II模型 III
OR值(95%CI)P值OR值(95%CI)P值OR值(95%CI)P值OR值(95%CI)P值
AIP(连续变量)3.15 (2.90~3.42)<0.0011.57 (1.43~1.73)<0.0011.52 (1.38~1.68)<0.0011.41 (1.27~ 1.57)<0.001
AIP 四分位
Q11.001.001.001.00
Q21.48 (1.35~1.62)<0.0011.07 (0.98~1.18)0.1271.07 (0.98~1.18)0.1381.06 (0.97~1.17)0.210
Q32.04 (1.88~2.22)<0.0011.22 (1.11~1.33)<0.0011.21 (1.10~1.32)<0.0011.17 (1.07~1.29)0.001
Q42.74 (2.53~2.97)<0.0011.41 (1.29~1.55)<0.0011.38 (1.26~1.51)<0.0011.29 (1.17~1.42)<0.001
P趋势值<0.001<0.001<0.001<0.001
), ArticleFig(id=1324078078653235345, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078073439715431, language=CN, label=表2, caption=

AIP与肾结石患病风险的多变量logistic回归分析

, figureFileSmall=null, figureFileBig=null, tableContent=
指标未调整模型模型 I模型 II模型 III
OR值(95%CI)P值OR值(95%CI)P值OR值(95%CI)P值OR值(95%CI)P值
AIP(连续变量)3.15 (2.90~3.42)<0.0011.57 (1.43~1.73)<0.0011.52 (1.38~1.68)<0.0011.41 (1.27~ 1.57)<0.001
AIP 四分位
Q11.001.001.001.00
Q21.48 (1.35~1.62)<0.0011.07 (0.98~1.18)0.1271.07 (0.98~1.18)0.1381.06 (0.97~1.17)0.210
Q32.04 (1.88~2.22)<0.0011.22 (1.11~1.33)<0.0011.21 (1.10~1.32)<0.0011.17 (1.07~1.29)0.001
Q42.74 (2.53~2.97)<0.0011.41 (1.29~1.55)<0.0011.38 (1.26~1.51)<0.0011.29 (1.17~1.42)<0.001
P趋势值<0.001<0.001<0.001<0.001
), ArticleFig(id=1324078078737121426, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078073439715431, language=EN, label=Table 3, caption=

Analysis of the saturation threshold effect between AIP and kidney stone risk using a two-stage linear regression model

, figureFileSmall=null, figureFileBig=null, tableContent=
结局OR值(95%CI)P值
肾结石
采用标准线性模型拟合1.66 (1.50~1.83)<0.001
采用两段线性模型拟合
<0.271.89 (1.62~2.19)<0.001
≥0.271.23 (1.10~1.37)<0.001
效应值差异0.65 (0.45~0.94)0.022
似然比检验0.021
), ArticleFig(id=1324078078812618899, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078073439715431, language=CN, label=表3, caption=

利用两段线性回归模型分析AIP与肾结石患病风险之间的饱和阈值效应

, figureFileSmall=null, figureFileBig=null, tableContent=
结局OR值(95%CI)P值
肾结石
采用标准线性模型拟合1.66 (1.50~1.83)<0.001
采用两段线性模型拟合
<0.271.89 (1.62~2.19)<0.001
≥0.271.23 (1.10~1.37)<0.001
效应值差异0.65 (0.45~0.94)0.022
似然比检验0.021
), ArticleFig(id=1324078078883922068, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078073439715431, language=EN, label=Table 4, caption=

Sensitivity analysis of the association between AIP and the risk of kidney stones

, figureFileSmall=null, figureFileBig=null, tableContent=
指标模型 1模型 2模型 3
OR值(95%CI)P值OR值(95%CI)P值OR值(95%CI)P值
AIP(连续变量)1.37 (1.21~1.54)<0.0011.60 (1.39~1.84)<0.0011.53 (1.38~1.71)<0.001
AIP四分位
Q11.001.001.00
Q21.09 (0.98~1.21)0.0951.14 (1.02~1.27)0.0211.12 (1.01~1.23)0.024
Q31.20 (1.08~1.34)0.0011.28 (1.14~1.44)<0.0011.28 (1.16~1.41)<0.001
Q41.29 (1.16~1.44)<0.0011.42 (1.26~1.61)<0.0011.42 (1.29~1.56)<0.001
P趋势值<0.001<0.001<0.001
), ArticleFig(id=1324078078959419541, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078073439715431, language=CN, label=表4, caption=

AIP与肾结石患病风险相关性的敏感性分析

, figureFileSmall=null, figureFileBig=null, tableContent=
指标模型 1模型 2模型 3
OR值(95%CI)P值OR值(95%CI)P值OR值(95%CI)P值
AIP(连续变量)1.37 (1.21~1.54)<0.0011.60 (1.39~1.84)<0.0011.53 (1.38~1.71)<0.001
AIP四分位
Q11.001.001.00
Q21.09 (0.98~1.21)0.0951.14 (1.02~1.27)0.0211.12 (1.01~1.23)0.024
Q31.20 (1.08~1.34)0.0011.28 (1.14~1.44)<0.0011.28 (1.16~1.41)<0.001
Q41.29 (1.16~1.44)<0.0011.42 (1.26~1.61)<0.0011.42 (1.29~1.56)<0.001
P趋势值<0.001<0.001<0.001
), ArticleFig(id=1324078079127191702, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078073439715431, language=EN, label=Table 5, caption=

Subgroup analysis of the association between AIP and the risk of kidney stones

, figureFileSmall=null, figureFileBig=null, tableContent=
亚组OR值(95%CI)P值P交互作用值
性别0.187
男性2.00 (1.81~2.21)<0.001
女性1.73 (1.43~2.10)<0.001
年龄(岁)0.651
<601.37 (1.21~1.54)<0.001
≥601.51 (1.20~1.91)0.001
BMI( kg/m2)0.067
<251.60 (1.39~1.84)<0.001
25~<301.42 (1.19~1.69)<0.001
≥301.14 (0.75~1.73)0.547
高血压0.052
否1.55 (1.35~1.77)<0.001
是1.65 (1.41~1.93)<0.001
糖尿病0.059
否1.53 (1.38~1.71)<0.001
是1.98 (1.44~2.71)<0.001
eGFR[ml/(min·1.73m2)]0.262
<901.56 (1.36~1.79)<0.001
90~<1201.45 (1.24~1.68)<0.001
≥1201.23 (0.86~1.74)0.255
), ArticleFig(id=1324078079223660695, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078073439715431, language=CN, label=表5, caption=

AIP与肾结石患病风险之间关联的亚组分析

, figureFileSmall=null, figureFileBig=null, tableContent=
亚组OR值(95%CI)P值P交互作用值
性别0.187
男性2.00 (1.81~2.21)<0.001
女性1.73 (1.43~2.10)<0.001
年龄(岁)0.651
<601.37 (1.21~1.54)<0.001
≥601.51 (1.20~1.91)0.001
BMI( kg/m2)0.067
<251.60 (1.39~1.84)<0.001
25~<301.42 (1.19~1.69)<0.001
≥301.14 (0.75~1.73)0.547
高血压0.052
否1.55 (1.35~1.77)<0.001
是1.65 (1.41~1.93)<0.001
糖尿病0.059
否1.53 (1.38~1.71)<0.001
是1.98 (1.44~2.71)<0.001
eGFR[ml/(min·1.73m2)]0.262
<901.56 (1.36~1.79)<0.001
90~<1201.45 (1.24~1.68)<0.001
≥1201.23 (0.86~1.74)0.255
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健康体检人群血浆致动脉硬化指数与肾结石风险的相关性分析
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许静鸣 1 , 沙维 2 , 李一帆 1
现代预防医学 | 流行病与统计方法 2026,53(3): 399-404
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现代预防医学 |流行病与统计方法 2026 , 53 (3) : 399 -404
健康体检人群血浆致动脉硬化指数与肾结石风险的相关性分析
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许静鸣(1986—),女,本科,主管护师,研究方向:泌尿系结石诊治及结石代谢的基础和转化研究

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许静鸣1, 沙维2 , 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 225000
  • 2.扬州大学医学院盱眙临床学院泌尿外科,江苏 盱眙 211700
通讯作者:
沙维,E-mail:
李一帆,E-mail:
沙维与李一帆为共同通信作者
作者简介:

许静鸣(1986—),女,本科,主管护师,研究方向:泌尿系结石诊治及结石代谢的基础和转化研究

Association between the atherogenic index of plasma and kidney stone risk in a health examination population
Jing-ming XU1, Wei SHA2 , Yi-fan LI1
Affiliations
  • Department of Urology, Affiliated Hospital of Yangzhou University, Yangzhou, Jiangsu 225000, China
出版时间: 2026-02-10 doi: 10.20043/j.cnki.MPM.202508337
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目的

探讨中国健康体检人群中血浆致动脉硬化指数(atherogenic index of plasma,AIP)与肾结石患病风险之间的关联,为风险分层及精准防控提供循证依据。

方法

基于2022年扬州大学附属医院88 001名健康体检者的横断面数据,通过多变量逻辑回归模型分析AIP与肾结石患病风险的关联性,并采用限制性立方样条和阈值饱和效应模型探索非线性关系。通过敏感性分析、亚组分析和E值评估结果的稳健性。

结果

本研究共纳入了88 001名数据完整的参与者。肾结石的总患病率为 6.82%,随着AIP的增加,肾结石的患病率呈上升趋势(P<0.001)。多变量逻辑回归分析结果显示,在调整了相关混杂因素后,最高AIP组与最低AIP组的人群相比,肾结石的风险增加了0.29倍(OR=1.29,95% CI:1.17~1.42,P<0.001)。AIP与肾结石风险呈非线性关联,当AIP<0.27时风险显著增加(OR=1.89,95% CI:1.62~2.19,P<0.001),而AIP≥0.27时风险增加趋势减缓(OR=1.23,95% CI:1.10~1.37,P<0.001)。分层分析显示,AIP与肾结石患病风险之间的相关性在年龄≥60岁、男性、糖尿病和高血压的参与者亚组中更明显。敏感性分析和交互作用检验表明,人群中AIP与肾结石风险的正相关是稳定和一致的(P均 >0.05)。

结论

这项研究表明,AIP与肾结石患病风险增加有关。AIP可作为评估肾结石风险的实用生物标志物,尤其对早期干预(如控制血脂、改善胰岛素抵抗)具有指导价值。未来需前瞻性研究验证其因果机制及阈值干预效果。

肾结石  /  血浆致动脉硬化指数  /  风险预测  /  横断面研究
Objective

To investigate the association between the atherogenic index of plasma(AIP) and the risk of kidney stones in the Chinese population undergoing health examinations, providing evidence-based support for risk stratification and precise prevention and control.

Methods

Using cross-sectional data from 88 001 healthy individuals who underwent health examinations at Yangzhou University Affiliated Hospital in 2022, we analyzed the association between AIP and the risk of kidney stones using a multivariable logistic regression model. We also explored the nonlinear relationship using restricted cubic splines and threshold saturation effect models. The robustness of the results was assessed through sensitivity analysis, subgroup analysis, and E-value assessment.

Results

A total of 88 001 participants with complete data were included in this study. The overall prevalence of kidney stones was 6.82%, and the prevalence of kidney stones increased with increasing AIP (P<0.001).Multivariable logistic regression analysis showed that, after adjusting for relevant confounding factors, the highest AIP group had 29% higher odds of kidney stones than the lowest AIP group (OR=1.29; 95% CI: 1.17-1.42; P<0.001). AIP exhibited a nonlinear association with kidney stone risk, with a significant increase in risk when AIP<0.27 (OR=1.89; 95% CI: 1.62-2.19; P<0.001), while the trend of increased risk slowed when AIP≥0.27 (OR=1.23; 95% CI: 1.10-1.37; P<0.001). Stratified analysis revealed that the association between AIP and the risk of kidney stones was more pronounced in subgroups of participants aged ≥60 years, men, and those with diabetes and hypertension. Sensitivity analyses and interaction tests indicated that the positive correlation between AIP and kidney stone risk was stable and consistent across the population (all P>0.05).

Conclusion

This study suggests that AIP is associated with an increased risk of kidney stones. AIP may serve as a practical biomarker for assessing kidney stone risk, particularly guiding early interventions such as lipid control and improving insulin resistance. Future prospective studies are needed to validate its causal mechanisms and threshold intervention effects.

Kidney stone  /  Atherogenic index of plasma  /  Risk prediction  /  Cross-sectional study
许静鸣, 沙维, 李一帆. 健康体检人群血浆致动脉硬化指数与肾结石风险的相关性分析. 现代预防医学, 2026 , 53 (3) : 399 -404 . DOI: 10.20043/j.cnki.MPM.202508337
Jing-ming XU, Wei SHA, Yi-fan LI. Association between the atherogenic index of plasma and kidney stone risk in a health examination population[J]. Modern Preventive Medicine, 2026 , 53 (3) : 399 -404 . DOI: 10.20043/j.cnki.MPM.202508337
肾结石是一种以尿路晶体异常沉积为特征的全球性高发疾病,其在全球范围内的患病率约为9%[1]。在中国,流行病学数据显示其发病率逐年增高且地理分布呈现显著差异性[2]。肾结石能够引发剧烈的肾绞痛、血尿、尿路感染等急性症状,显著影响患者生活质量并带来沉重的医疗资源消耗[3]。越来越多的证据表明,肾结石的形成是一个复杂的病理生理过程,其中代谢性紊乱扮演着核心驱动角色[4]。血脂谱的异常被多项研究证实为肾结石形成的显著风险因素[5]。这一关联提示脂质代谢通路可能通过炎症诱导、氧化应激加剧或结晶界面活性改变等多种途径,影响尿液过饱和状态或结晶动力学过程。
血浆致动脉硬化指数(atherogenic index of plasma,AIP)作为一个复合性脂质指标,能够灵敏反映致动脉粥样硬化的关键脂蛋白亚型分布[6]。相较于单一血脂参数,AIP整合了促动脉粥样硬化的脂质组分,在预测心血管疾病风险方面展现出卓越价值[7-8]。近期研究焦点开始转向探索AIP与肾结石之间的关系。初步观察显示结石患者往往伴有较高的AIP值,这暗示着肾结石与动脉硬化性疾病可能存在共享的代谢性病理基础。
然而,现有证据存在明显的局限。首先,既往研究多聚焦于线性模型评估AIP与肾结石风险间的关联,却忽略了人体生物学效应中普遍存在的非线性或阈值现象——即关联强度可能在AIP的特定范围发生动态变化,呈现“U”型、“J”型或平台型曲线特征[9]。其次,专门针对中国人群进行的前瞻性研究较为有限,且对混杂因素的校正常不够充分[10]。因此,本研究基于扬州大学附属医院健康体检人群的横断面数据,通过限制性立方样条回归(restricted cubic splines, RCS)和饱和阈值效应分析,深入探讨AIP与肾结石患病风险的非线性剂量-反应关系。通过这一探索,不仅期望揭示AIP在肾结石发生中的潜在生物学机制窗口,更致力于为临床实践中基于AIP的风险分层及精准防控策略提供更为可靠的循证支持。
收集并整理2022年1—12月在扬州大学附属医院进行健康体检的113 947人的体检信息。排除年龄小于18岁、未接受腹部超声检查、甘油三酯或高密度脂蛋白胆固醇(high-density lipoprotein cholesterol,HDL-C)数据缺失的参与者,最终共纳入88 001名受试者。
由专业的医疗保健专业人员在标准环境中对参与者进行身高、体重和血压的测量。身体质量指数(body mass index,BMI)通过体重/身高的二次方(kg/m2)计算得到。所有参与者在进行血液生化检查前需要至少禁食10 h。由专业护理人员采集被体检者的静脉血,随后在标准实验室中使用自动分析仪器(Beckman Coulter AU5800,Brea,CA,USA)测量高密度脂蛋白胆固醇HDL-C、总蛋白、甘油三酯 、总胆固醇 、低密度脂蛋白胆固醇(low-density lipoprotein cholesterol,LDL-C)、血清肌酐、血尿素氮 、尿酸和空腹血糖水平。
AIP的计算公式为AIP=Lg [TG(mmol/L)/HDL-C(mmol/L)]。其中,TG为甘油三酯。
肾结石:参与者由经验丰富的中级或更高级别的超声医师进行腹部超声检查(使用腹部凸面阵列探头,频率:3.5~5 MHz),诊断标准为在肾脏区域发现伴有声影的强回声团。
糖尿病被定义为空腹血糖≥ 7.0 mmol/L、糖化血红蛋白(glycated hemoglobin,HbA1c)≥ 6.5%、使用降糖药物或自我报告患有糖尿病的个体。如果满足以下标准之一,则认为存在高血压:(1)平均舒张压为90 mmHg或以上;(2)平均收缩压为140 mmHg或以上;(3)使用抗高血压药物;(4)受试者自我报告患有高血压。使用CKD-EPI 方程计算体检人群的估计肾小球滤过率(estimated glomerular filtration rate,eGFR)[11]。
使用R软件(4.2.3)和Empower Stats(6.0)进行统计分析。连续变量通过(均值±标准差)表示,并使用单因素方差分析和Mann-Whitney检验进行比较。分类变量以频率或百分比表示,并使用χ2检验进行比较。在这项研究中,5个变量存在缺失值。其中总胆固醇、LDL-C、空腹血糖、收缩压、舒张压分别缺失326、209、1 256、609和609例。使用R软件中的mice包进行多重插补,插补模型使用预测均值匹配(prediction mean matching,PMM)方法,默认线性回归模型。方差膨胀因子(variance inflation factor,VIF)小于5且与肾结石显著相关的协变量被纳入最终模型。通过四种logistic回归模型(未调整模型、模型I、模型II和模型III)分析AIP与肾结石患病风险的相关性。其中模型I对性别、年龄、BMI进行了调整;模型II对性别、年龄、BMI、高血压、糖尿病进行了调整;模型III调整了性别、年龄、BMI、糖尿病、高血压、总胆固醇、LDL-C、尿酸、空腹血糖、总蛋白、血清肌酐和eGFR。通过构建RCS曲线和阈值饱和效应分析来探索AIP与肾结石患病风险之间的非线性关系。通过多种敏感性分析检验结果的可靠性。此外,通过计算E值以排除AIP和肾结石之间未知混杂因素的可能性。最后,通过分层分析和交互作用检验分析特定人群中AIP和肾结石之间相关性的稳定性。检验水准α=0.05,双侧检验。
本研究遵循赫尔辛基宣言的伦理指南,并由扬州大学附属医院伦理委员会批准(批准号:2023-YKL01-13)。本研究为回顾性分析且所有数据均已去标识化,伦理委员会特此免除了获取体检者知情同意的要求。
共纳入了 88 001名数据完整的参与者,其中男性占56.9%。所有参与者的平均年龄为(46.9±13.5)岁。根据AIP四分位数将所有参与者分为四组,分别为Q1 (AIP<-0.20)、Q2(-0.20 ≤AIP<0.02)、Q3(0.02≤AIP<0.26)和Q4(AIP≥ 0.26)。肾结石的总患病率为 6.82%;随着 AIP 的增加,肾结石的患病率显着上升,从3.9%(Q1)升高到10.0%(Q4)(P<0.001)。与低AIP水平的参与者相比,高AIP水平者的年龄较大,他们也更有可能患有代谢相关疾病包括糖尿病、高血压和肥胖(P<0.001)。所有参与者的特征见表1。
采用多因素logistic回归分析研究AIP与肾结石患病风险之间的关系,见表2。在未调整的模型中,AIP与肾结石患病风险呈显著正相关。在调整了性别、年龄、BMI后,AIP每增加一个单位,肾结石的患病率增加57%(OR=1.57,95% CI:1.43~1.73,P<0.001)。进一步调整了糖尿病和高血压后,AIP与肾结石患者风险的相关性仍然显著(OR=1.52,95% CI: 1.38~1.68,P<0.001)。在模型III中,与AIP最低四分位数的参与者相比,AIP最高四分位数的参与者肾结石患病率增加了29%(OR=1.29,95% CI:1.17~1.42,P<0.001)。
通过RCS和阈值饱和效应分析以探索AIP与肾结石患病风险之间是否存在非线性关系。标准线性模型和双相线性模型之间似然比检验的P<0.05,这表明AIP和肾结石风险之间存在非线性关系,见表3。结合递归算法的双相线性模型确定AIP的拐点为0.27。当AIP<0.27时,AIP比值每增加一个单位,肾结石的患病风险增加0.89倍(OR=1.89,95% CI: 1.62~2.19,P<0.001)。相反,当AIP≥0.27时,肾结石风险增加趋势减缓(OR=1.23,95% CI: 1.10~1.37,P<0.001)。见图1。
通过多种敏感性分析评估先前发现的稳健性。首先,分析年龄小于60岁的个体。在调整混杂协变量后,AIP与肾结石之间仍然存在正相关(OR=1.37,95% CI: 1.12~1.54,P<0.001)(模型1)。排除BMI ≥25 kg/m2的个体后,分析显示AIP仍与肾结石呈正相关(OR=1.60,95% CI: 1.39~1.84)(模型2)。在非糖尿病的个体中,AIP每增加一个单位,肾结石的患病风险增加53%(95% CI: 1.38~1.71,P<0.001)(模型3)。此外,计算的E值为2.17,这显示了更高水平的统计学显著性,表明未识别的混杂因素可以忽略不计。见表4。
通过亚组分析评估AIP与肾结石之间关系的稳定性,并确定不同人群之间的潜在差异,见表5。值得注意的是,AIP与肾结石之间的相关性在年龄 ≥60岁、男性、糖尿病和高血压的参与者亚组中更明显(P<0.05)。交互作用检验显示,所有亚组中的P值均大于0.05。上述的研究结果表明在一般人群中,AIP与肾结石患病风险之间的正相关性是稳定且一致的。尽管某些亚组(如男性、年龄≥60岁)中AIP与肾结石的关联强度更高,但交互作用检验均未达到统计学显著性(P>0.05),提示这些差异可能源于趋势而非显著效应修饰。
本研究通过系统的分析体检人群的数据证实AIP与肾结石患病风险呈显著正相关,且存在非线性阈值效应。多因素调整模型显示,AIP每增加1单位,肾结石风险上升41%。限制性立方样条回归揭示其拐点为0.27,当AIP<0.27时风险急剧上升(OR=1.89),而当AIP≥0.27时,AIP与肾结石之间风险增加的趋势减缓。这一非线性模式突破既往线性模型的局限,提示AIP对结石风险的影响存在生物学效应窗口。然后,由于横断面研究的局限性,不能得出AIP和肾结石风险之间的因果关系,未来需开展前瞻性队列研究或孟德尔随机化分析以进一步验证AIP与肾结石之间的因果关联。
这项研究发现AIP升高伴随尿酸、空腹血糖及BMI的梯度上升,提示AIP可能通过多种代谢途径影响肾结石形成。较高的甘油三酯竞争性抑制肾小管尿酸排泄,升高尿酸饱和度,促进结石的形成[12]。而HDL-C降低削弱了机体抑制草酸钙晶体聚集的生理功能[13]。最近的研究发现AIP是反应IR的综合指标。IR可引起肾脏近端小管钠/氢交换功能障碍,影响氨转运或离子捕获,导致尿液低pH与枸橼酸重吸收增多[14]。此外,IR 会增加肾脏对钙、磷和尿酸的排泄[14]。RCS回归所揭示的拐点现象(AIP=0.27)暗示了脂质代谢紊乱与肾结石风险之间存在生物阈值效应。当AIP低于0.27时,机体可能仍处于代谢代偿期——轻微升高的甘油三酯与轻度降低的HDL-C虽已启动病理进程,但尚未触发有效的负反馈调节机制。此阶段,甘油三酯的微幅上升即可竞争性抑制肾小管尿酸排泄,导致尿液中尿酸饱和度显著升高[15]。同时,HDL-C的微量减少可能会削弱其抑制草酸钙结晶的能力,双重打击使晶体成核的动力学阈值被突破,肾结石风险呈现指数级增长(OR=1.89)。当AIP超过0.27后,机体可能启动多种适应性反应。首先,代谢重编使得脂毒性通路趋于饱和,如肝脏生成更多胆固醇酯转移蛋白(cholesteryl ester transfer protein,CE总蛋白)以加速胆固醇逆向转运,从而部分抵消HDL-C降低的危害[16]。其次,继发性病理代偿开始主导,例如高AIP常伴随的IR会激活肾小管上皮中的Na+/H+交换体(Na+/H+ exchanger,NHE),使尿液持续酸化[17]。最后,高AIP人群常合并高血压、糖尿病等共病,降压药物(如噻嗪类利尿剂)可能通过促进尿钙重吸收,掩盖AIP本身的影响[18]。此外,超声对肥胖人群的小结石检出率下降,也可能导致高风险群体的假性平台化现象。这一拐点的存在具有重要临床启示,AIP在0.27以下的亚临床范围为防控关键窗口,针对此阶段人群的降脂干预(如贝特类药物提升HDL-C功能)可能显著阻断结石形成链。而AIP≥0.27后风险增长的趋缓,恰说明结石预防策略需转向综合管理(如联合尿液碱化与血糖控制),而非单纯依赖脂质调控。
本研究存在一些局限性。横断面设计难以确立因果时序、缺乏饮食及尿液成分数据。此外,超声检查对直径<3 mm的肾结石检出敏感性有限,可能导致患病率被低估,未来可结合CT等更敏感影像学方法以提高诊断准确性。值得注意的是,该研究的人群为中国人。因此,这项研究的结果可能不能直接适用于其他种族。这项研究的优势在于使用了具有代表性的大样本,这增强了研究结果的可信度。此外,通过严谨的方法学设计综合分析了AIP与肾结石的关系。然而,由于横断面研究的固有局限性,观察到的AIP和肾结石之间的联系只能被认为是相关的。未来需前瞻性队列验证AIP对结石发病率及复发的预测效能,并探索脂代谢干预对结石预防的实际效果。
这项研究表明,AIP升高与肾结石患病风险增加有关。AIP可作为评估肾结石风险的实用生物标志物。该非线性关联提示脂质代谢紊乱是肾结石发生的关键病理基础,拐点0.27为临床风险分层提供量化依据。建议将AIP整合至肾结石早期筛查体系,通过靶向调控脂代谢通路降低疾病负担。未来需前瞻性研究验证其因果机制及阈值干预效果。
  • 国家自然科学基金(82002675)
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doi: 10.20043/j.cnki.MPM.202508337
  • 接收时间:2025-08-20
  • 首发时间:2026-11-02
  • 出版时间:2026-02-10
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  • 收稿日期:2025-08-20
基金
国家自然科学基金(82002675)
作者信息
    1.扬州大学附属医院泌尿外科,江苏 扬州 225000
    2.扬州大学医学院盱眙临床学院泌尿外科,江苏 盱眙 211700

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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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