Article(id=1211268930421780825, tenantId=1146029695717560320, journalId=1189873630562394117, issueId=1211268928383348982, articleNumber=null, orderNo=null, doi=10.11855/j.issn.0577-7402.2021.02.10, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1601136000000, receivedDateStr=2020-09-27, revisedDate=1608652800000, revisedDateStr=2020-12-23, acceptedDate=null, acceptedDateStr=null, onlineDate=1766718617300, onlineDateStr=2025-12-26, pubDate=1614441600000, pubDateStr=2021-02-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1766718617300, onlineIssueDateStr=2025-12-26, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1766718617300, creator=13701087609, updateTime=1766718617300, updator=13701087609, issue=Issue{id=1211268928383348982, tenantId=1146029695717560320, journalId=1189873630562394117, year='2021', volume='46', issue='2', pageStart='107', pageEnd='211', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1766718616815, creator=13701087609, updateTime=1766718805938, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1211269721685627740, tenantId=1146029695717560320, journalId=1189873630562394117, issueId=1211268928383348982, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1211269721685627741, tenantId=1146029695717560320, journalId=1189873630562394117, issueId=1211268928383348982, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=169, endPage=174, ext={EN=ArticleExt(id=1211268930761519463, articleId=1211268930421780825, tenantId=1146029695717560320, journalId=1189873630562394117, language=EN, title=Correlation between serum non-high-density lipoprotein cholesterol/high-density lipoprotein cholesterol and microalbuminuria in patients with type 2 diabetes, columnId=1190310109000602400, journalTitle=Medical Journal of Chinese People’s Liberation Army, columnName=Clinical Research, runingTitle=null, highlight=null, articleAbstract=

Objective To investigate the correlation between the ratio of serum non-high-density lipoprotein cholesterol/high-density lipoprotein cholesterol (Non-HDL-C/HDL-C) and microalbuminuria in patients with type 2 diabetes mellitus(T2DM). Methods A total of 576 patients with T2DM hospitalized in the Endocrinology Department of the First Affiliated Hospital of Chongqing Medical University from 2017 to 2019 were included in present study (373 males and 203 females, aged 59±11.8 years). The clinical data of patients were collected including: age, height, weight, history of smoking, duration of diabetes, glycosylated hemoglobin (HbA1c), comorbidities, medication status, blood lipid level, etc. The values of non-high-density lipoprotein cholesterol (Non-HDL-C) and the ratio of Non-HDL-C and HDL-C (Non-HDL-C/HDL-C) were then calculated. Depending on whether they have microalbuminuria and diabetic retinopathy, patients were divided into groups for comparative statistical analysis, the binary logistic regression model was used to analyze the influencing factors of microalbuminuria and diabetic retinopathy. Finally, the receiver operating characteristic curve (ROC) was adopted to analyze the predictive value of Non-HDL-C/HDL-C for microalbuminuria. Results The age, course of disease, systolic blood pressure, diastolic blood pressure, creatinine, total cholesterol (TC), triglyceride (TG), Non-HDL-C, Non-HDL-C/HDL-C, hypertension and the use of angiotensin-converting enzyme inhibitor (ACEI)/angiotensin receptor blocker (ARB) hypotensive drugs were all higher, but the HDL-C was lower in microalbuminuria group than those in non-proteinuria group with statistically significant differences (P<0.05). The course of disease longer and the proportion of hypertension is higher in retinopathy group than those in non-nephropathy group, with statistically significant differences (P<0.05). The logistic regression analysis revealed that high systolic blood pressure, high creatinine level, and high Non-HDL-C/HDL-C ratio were the influencing factors for the occurrence of microalbuminuria (P<0.05). The area under the ROC curve (AUC) of combined systolic blood pressure, creatinine level and Non-HDL-C/HDL-C to predict microalbuminuria is 0.774. Conclusions A large Non-HDL-C/HDL-C ratio is a risk factor for occurrence of microalbuminuria in patients with T2DM. Combined detection of systolic blood pressure, creatinine level and Non-HDL-C/HDL-C has a high predictive value for microalbuminuria, so is worthy of clinical reference.

, correspAuthors=Wei Ren, authorNote=null, correspAuthorsNote=
*E-mail:
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目的 探讨2型糖尿病(T2DM)患者血清非高密度脂蛋白胆固醇与高密度脂蛋白胆固醇比值(Non-HDL-C/HDL-C)与微量白蛋白尿的相关性。方法 选取2017—2019年在重庆医科大学附属第一医院内分泌科住院的T2DM患者共576例,其中男373例,女203例,年龄(59±11.8)岁。收集患者的临床资料,包括年龄、身高、体重、吸烟史、糖尿病病程、糖化血红蛋白(HbA1c)、合并症、用药情况、血脂水平等。通过计算得到非高密度脂蛋白胆固醇(Non-HDL-C)值、Non-HDL-C/HDL-C。分别根据患者有无微量白蛋白尿、有无糖尿病视网膜病变进行分组分析,并采用二元logistic回归分析发生微量白蛋白尿及糖尿病视网膜病变的影响因素。采用受试者工作特征曲线(ROC)分析Non-HDL-C/HDL-C对微量白蛋白尿的预测价值。结果 微量白蛋白尿组的年龄、病程、收缩压、舒张压、肌酐、总胆固醇(TC)、三酰甘油(TG)、Non-HDL-C、Non-HDL-C/HDL-C、高血压、使用血管紧张素转化酶抑制剂(ACEI)/血管紧张素Ⅱ受体阻滞剂(ARB)类降压药物的情况均高于无白蛋白尿组,HDL-C低于无白蛋白尿组,差异有统计学意义(P<0.05)。与无视网膜病变组比较,视网膜病变组病程更长,高血压比例更高,差异有统计学意义(P<0.05)。logistic回归分析结果显示,收缩压高、肌酐高、Non-HDL-C/HDL-C大是发生微量白蛋白尿的危险因素(P<0.05)。收缩压、肌酐、Non-HDL-C/HDL-C联合预测微量白蛋白尿的ROC曲线下面积(AUC)为0.774。结论 T2DM患者Non-HDL-C与HDL-C比值大是发生微量白蛋白尿的危险因素,收缩压、肌酐、Non-HDL-C/HDL-C联合检测对微量白蛋白尿有较高的预测价值,值得临床借鉴。

, correspAuthors=任伟, authorNote=null, correspAuthorsNote=
任伟,E-mail:
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钱文杰,硕士研究生,主要从事糖尿病及其并发症防治方面的研究。E-mail:

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Eye(Lond), 2011, 25(7): 843-849., articleTitle=Medical management of diabetic retinopathy: fenofibrate and ACCORD Eye studies, refAbstract=null), Reference(id=1211268943487038253, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1211268930421780825, doi=null, pmid=null, pmcid=null, year=2012, volume=32, issue=2, pageStart=322, pageEnd=329, url=null, language=null, rfNumber=[23], rfOrder=25, authorNames=Xu J, Wei WB, Yuan MX, journalName=Retina, refType=null, unstructuredReference=Xu J, Wei WB, Yuan MX, et al. Prevalence and risk factors for diabetic retinopathy: the Beijing Communities Diabetes Study 6[J]. Retina, 2012, 32(2): 322-329., articleTitle=Prevalence and risk factors for diabetic retinopathy: the Beijing Communities Diabetes Study 6, refAbstract=null), Reference(id=1211268943583507248, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1211268930421780825, doi=null, pmid=null, pmcid=null, year=2017, volume=10, issue=6, pageStart=948, pageEnd=954, url=null, language=null, rfNumber=[24], rfOrder=26, authorNames=Elwali ES, Almobarak AO, Hassan MA, journalName=Int J Ophthalmol, refType=null, unstructuredReference=Elwali ES, Almobarak AO, Hassan MA, et al. Frequency of diabetic retinopathy and associated risk factors in Khartoum, Sudan: population based study[J]. Int J Ophthalmol, 2017, 10(6): 948-954., articleTitle=Frequency of diabetic retinopathy and associated risk factors in Khartoum, Sudan: population based study, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1211268931638129068, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1211268930421780825, xref=null, ext=[AuthorCompanyExt(id=1211268931646517677, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1211268930421780825, companyId=1211268931638129068, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Department of Endocrinology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China), AuthorCompanyExt(id=1211268931650711982, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1211268930421780825, companyId=1211268931638129068, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=重庆医科大学附属第一医院内分泌科,重庆 400010)])], figs=[ArticleFig(id=1211268935626912384, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1211268930421780825, language=EN, label=Fig.1, caption=The ROC curve of systolic blood pressure, creatinine level and Non-HDL-C/HDL-C in predicting microalbuminuria in T2DM patients, figureFileSmall=M1jX8AATFq9p2RNk9/Agkg==, figureFileBig=WTDaRAfCodYZy+tqZpLSZw==, tableContent=null), ArticleFig(id=1211268935689826948, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1211268930421780825, language=CN, label=图1, caption=2型糖尿病患者收缩压、肌酐、Non-HDL-C/HDL-C联合预测微量白蛋白尿的ROC曲线, figureFileSmall=M1jX8AATFq9p2RNk9/Agkg==, figureFileBig=WTDaRAfCodYZy+tqZpLSZw==, tableContent=null), ArticleFig(id=1211268935912125078, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1211268930421780825, language=EN, label=Tab.1, caption=

Comparison of the clinical data between non-proteinuria and microacbuminuria group

, figureFileSmall=null, figureFileBig=null, tableContent=
项目无白蛋白尿组(n=449)微量白蛋白尿组(n=127)ZP
女/男(例)155/29448/79–0.6810.496
年龄[岁,M(Q1Q3)]57(51,66)61(54,70)–3.0700.002
身高[cm,M(Q1Q3)]165(158,170)164(158,168)–0.8120.417
体重[kg,M(Q1Q3)]65.00(57.03,75.45)67.25(59.75,74.00)–1.5830.114
吸烟史[例(%)]208(46.3)59(46.5)–0.0260.979
病程[月,M(Q1Q3)]96(36,156)120(48,168)–2.7220.006
HbA1c[%,M(Q1Q3)]9.0(7.5,11.0)9.5(7.7,10.9)–0.8590.390
收缩压[mmHg,M(Q1Q3)]130(119,142)144(129,159)–6.914<0.001
舒张压[mmHg,M(Q1Q3)]78(70,86)82(73,92)–2.9480.003
尿素[mmol/L,M(Q1Q3)]5.8(4.8,6.8)5.7(5.0,7.2)–1.4410.150
肌酐[μmol/L,M(Q1Q3)]65.0(55.0,74.0)72.0(60.5,90.0)–4.134<0.001
TC[mmol/L,M(Q1Q3)]4.14(3.44,4.90)4.47(3.60,5.22)–2.0590.039
TG[mmol/L,M(Q1Q3)]1.59(1.09,2.31)1.91(1.28,3.49)–2.7010.007
HDL-C[mmol/L,M(Q1Q3)]1.03(0.86,1.23)0.96(0.79,1.17)–2.4560.014
LDL-C[mmol/L,M(Q1Q3)]2.52(1.82,3.24)2.51(1.90,3.23)–0.1230.902
Non-HDL-C[mmol/L,M(Q1Q3)]3.10(2.40,3.79)3.37(2.68,4.35)–2.6260.009
Non-HDL-C/HDL-C[M(Q1Q3)]2.98(2.11,3.95)3.64(2.36,4.71)–3.484<0.001
高血压[例(%)]189(42.1)90(70.9)–5.723<0.001
服用降脂药物[例(%)]364(81.1)111(87.4)–1.6550.098
使用ACEI/ARB[例(%)]131(29.2)58(45.7)–3.492<0.001
), ArticleFig(id=1211268936012788383, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1211268930421780825, language=CN, label=表1, caption=

无白蛋白尿组与微量白蛋白尿组临床资料比较

, figureFileSmall=null, figureFileBig=null, tableContent=
项目无白蛋白尿组(n=449)微量白蛋白尿组(n=127)ZP
女/男(例)155/29448/79–0.6810.496
年龄[岁,M(Q1Q3)]57(51,66)61(54,70)–3.0700.002
身高[cm,M(Q1Q3)]165(158,170)164(158,168)–0.8120.417
体重[kg,M(Q1Q3)]65.00(57.03,75.45)67.25(59.75,74.00)–1.5830.114
吸烟史[例(%)]208(46.3)59(46.5)–0.0260.979
病程[月,M(Q1Q3)]96(36,156)120(48,168)–2.7220.006
HbA1c[%,M(Q1Q3)]9.0(7.5,11.0)9.5(7.7,10.9)–0.8590.390
收缩压[mmHg,M(Q1Q3)]130(119,142)144(129,159)–6.914<0.001
舒张压[mmHg,M(Q1Q3)]78(70,86)82(73,92)–2.9480.003
尿素[mmol/L,M(Q1Q3)]5.8(4.8,6.8)5.7(5.0,7.2)–1.4410.150
肌酐[μmol/L,M(Q1Q3)]65.0(55.0,74.0)72.0(60.5,90.0)–4.134<0.001
TC[mmol/L,M(Q1Q3)]4.14(3.44,4.90)4.47(3.60,5.22)–2.0590.039
TG[mmol/L,M(Q1Q3)]1.59(1.09,2.31)1.91(1.28,3.49)–2.7010.007
HDL-C[mmol/L,M(Q1Q3)]1.03(0.86,1.23)0.96(0.79,1.17)–2.4560.014
LDL-C[mmol/L,M(Q1Q3)]2.52(1.82,3.24)2.51(1.90,3.23)–0.1230.902
Non-HDL-C[mmol/L,M(Q1Q3)]3.10(2.40,3.79)3.37(2.68,4.35)–2.6260.009
Non-HDL-C/HDL-C[M(Q1Q3)]2.98(2.11,3.95)3.64(2.36,4.71)–3.484<0.001
高血压[例(%)]189(42.1)90(70.9)–5.723<0.001
服用降脂药物[例(%)]364(81.1)111(87.4)–1.6550.098
使用ACEI/ARB[例(%)]131(29.2)58(45.7)–3.492<0.001
), ArticleFig(id=1211268937241719460, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1211268930421780825, language=EN, label=Tab.2, caption=

Comparison of the clinical data between non-retinopathy group and retinopathy group

, figureFileSmall=null, figureFileBig=null, tableContent=
项目无视网膜病变组(n=413)视网膜病变组(n=115)ZP
女/男(例)134/27947/68–1.6820.093
年龄[岁,M(Q1Q3)]57(51,66)59(54,67)–1.3460.178
身高[cm,M(Q1Q3)]165(158,170)164(156,168)–1.1560.248
体重[kg,M(Q1Q3)]65.00(58.13,72.89)65.00(55.00,72.00)–1.1590.247
吸烟史[例(%)]198(47.9)47(40.9)–1.3440.179
病程[月,M(Q1Q3)]84(36,156)120(48,180)–2.3530.019
HbA1c[%,M(Q1Q3)]8.9(7.5,11.1)9.7(7.0,10.9)–1.0190.308
收缩压[mmHg,M(Q1Q3)]130(120,143)136(120,146)–1.6870.092
舒张压[mmHg,M(Q1Q3)]78(71,87)79(72,88)–0.3560.722
尿素[mmol/L,M(Q1Q3)]5.7(4.8,6.8)6.0(4.8,7.2)–1.0110.312
肌酐[μmol/L,M(Q1Q3)]66(56,77)65(55,76)–0.4650.642
TC[mmol/L,M(Q1Q3)]4.18(3.44,4.98)4.18(3.62,4.93)–0.9890.323
TG[mmol/L,M(Q1Q3)]1.62(1.11,2.61)1.44(1.15,2.28)–1.2020.229
HDL-C[mmol/L,M(Q1Q3)]1.00(0.85,1.22)1.03(0.82,1.23)–0.0980.922
LDL-C[mmol/L,M(Q1Q3)]2.52(1.81,3.19)2.52(2.09,3.33)–1.2250.221
Non-HDL-C[mmol/L,M(Q1Q3)]3.11(2.43,3.82)3.16(2.60,3.85)–1.1180.264
Non-HDL-C/HDL-C[M(Q1Q3)]3.07(2.12,4.02)3.15(2.24,4.24)–0.8880.375
高血压[例(%)]183(44.3)63(54.8)–1.9890.047
服用降脂药物[例(%)]334(80.9)99(86.1)–1.2860.198
使用ACEI/ARB[例(%)]126(30.5)44(38.3)–1.5720.116
), ArticleFig(id=1211268937367548586, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1211268930421780825, language=CN, label=表2, caption=

无视网膜病变组与视网膜病变组临床资料比较

, figureFileSmall=null, figureFileBig=null, tableContent=
项目无视网膜病变组(n=413)视网膜病变组(n=115)ZP
女/男(例)134/27947/68–1.6820.093
年龄[岁,M(Q1Q3)]57(51,66)59(54,67)–1.3460.178
身高[cm,M(Q1Q3)]165(158,170)164(156,168)–1.1560.248
体重[kg,M(Q1Q3)]65.00(58.13,72.89)65.00(55.00,72.00)–1.1590.247
吸烟史[例(%)]198(47.9)47(40.9)–1.3440.179
病程[月,M(Q1Q3)]84(36,156)120(48,180)–2.3530.019
HbA1c[%,M(Q1Q3)]8.9(7.5,11.1)9.7(7.0,10.9)–1.0190.308
收缩压[mmHg,M(Q1Q3)]130(120,143)136(120,146)–1.6870.092
舒张压[mmHg,M(Q1Q3)]78(71,87)79(72,88)–0.3560.722
尿素[mmol/L,M(Q1Q3)]5.7(4.8,6.8)6.0(4.8,7.2)–1.0110.312
肌酐[μmol/L,M(Q1Q3)]66(56,77)65(55,76)–0.4650.642
TC[mmol/L,M(Q1Q3)]4.18(3.44,4.98)4.18(3.62,4.93)–0.9890.323
TG[mmol/L,M(Q1Q3)]1.62(1.11,2.61)1.44(1.15,2.28)–1.2020.229
HDL-C[mmol/L,M(Q1Q3)]1.00(0.85,1.22)1.03(0.82,1.23)–0.0980.922
LDL-C[mmol/L,M(Q1Q3)]2.52(1.81,3.19)2.52(2.09,3.33)–1.2250.221
Non-HDL-C[mmol/L,M(Q1Q3)]3.11(2.43,3.82)3.16(2.60,3.85)–1.1180.264
Non-HDL-C/HDL-C[M(Q1Q3)]3.07(2.12,4.02)3.15(2.24,4.24)–0.8880.375
高血压[例(%)]183(44.3)63(54.8)–1.9890.047
服用降脂药物[例(%)]334(80.9)99(86.1)–1.2860.198
使用ACEI/ARB[例(%)]126(30.5)44(38.3)–1.5720.116
), ArticleFig(id=1211268937468211888, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1211268930421780825, language=EN, label=Tab.3, caption=

Logistic regression analysis of influencing factors for microalbuminuria

, figureFileSmall=null, figureFileBig=null, tableContent=
因素βSEWaldχ2POR(95%Cl)
年龄0.0070.0130.3130.5761.007(0.982~1.033)
病程0.0020.0011.6300.2021.002(0.999~1.005)
收缩压0.0420.00826.786<0.0011.043(1.027~1.059)
舒张压–0.0020.0120.0400.8410.998(0.975~1.020)
肌酐0.0210.00515.888<0.0011.021(1.011~1.032)
TG–0.0490.0740.4500.5020.952(0.824~1.100)
高血压–0.4360.3062.0330.1540.647(0.355~1.177)
服用降脂药物0.0320.3390.0090.9251.033(0.531~2.008)
使用ACEI/ARB类降压药物0.0280.2820.0100.9211.028(0.592~1.788)
Non-HDL-C0.2600.1590.0260.8721.026(0.752~1.401)
Non-HDL-C/HDL-C0.2910.1275.2140.0221.338(1.042~1.717)
), ArticleFig(id=1211268937581458102, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1211268930421780825, language=CN, label=表3, caption=

2型糖尿病患者微量白蛋白尿影响因素的logistic回归分析

, figureFileSmall=null, figureFileBig=null, tableContent=
因素βSEWaldχ2POR(95%Cl)
年龄0.0070.0130.3130.5761.007(0.982~1.033)
病程0.0020.0011.6300.2021.002(0.999~1.005)
收缩压0.0420.00826.786<0.0011.043(1.027~1.059)
舒张压–0.0020.0120.0400.8410.998(0.975~1.020)
肌酐0.0210.00515.888<0.0011.021(1.011~1.032)
TG–0.0490.0740.4500.5020.952(0.824~1.100)
高血压–0.4360.3062.0330.1540.647(0.355~1.177)
服用降脂药物0.0320.3390.0090.9251.033(0.531~2.008)
使用ACEI/ARB类降压药物0.0280.2820.0100.9211.028(0.592~1.788)
Non-HDL-C0.2600.1590.0260.8721.026(0.752~1.401)
Non-HDL-C/HDL-C0.2910.1275.2140.0221.338(1.042~1.717)
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2型糖尿病患者血清Non-HDL-C/HDL-C与微量白蛋白尿的相关性
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钱文杰 , 陈月 , 王蕴婷 , 任子愚 , 伍询 , 任伟 *
解放军医学杂志 | 临床研究 2021,46(2): 169-174
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解放军医学杂志 | 临床研究 2021, 46(2): 169-174
2型糖尿病患者血清Non-HDL-C/HDL-C与微量白蛋白尿的相关性
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钱文杰 , 陈月, 王蕴婷, 任子愚, 伍询, 任伟*
作者信息
  • 重庆医科大学附属第一医院内分泌科,重庆 400010
  • 钱文杰,硕士研究生,主要从事糖尿病及其并发症防治方面的研究。E-mail:

通讯作者:

任伟,E-mail:
Correlation between serum non-high-density lipoprotein cholesterol/high-density lipoprotein cholesterol and microalbuminuria in patients with type 2 diabetes
Wen-Jie Qian , Yue Chen, Yun-Ting Wang, Zi-Yu Ren, Xun Wu, Wei Ren*
Affiliations
  • Department of Endocrinology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China
出版时间: 2021-02-28 doi: 10.11855/j.issn.0577-7402.2021.02.10
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目的 探讨2型糖尿病(T2DM)患者血清非高密度脂蛋白胆固醇与高密度脂蛋白胆固醇比值(Non-HDL-C/HDL-C)与微量白蛋白尿的相关性。方法 选取2017—2019年在重庆医科大学附属第一医院内分泌科住院的T2DM患者共576例,其中男373例,女203例,年龄(59±11.8)岁。收集患者的临床资料,包括年龄、身高、体重、吸烟史、糖尿病病程、糖化血红蛋白(HbA1c)、合并症、用药情况、血脂水平等。通过计算得到非高密度脂蛋白胆固醇(Non-HDL-C)值、Non-HDL-C/HDL-C。分别根据患者有无微量白蛋白尿、有无糖尿病视网膜病变进行分组分析,并采用二元logistic回归分析发生微量白蛋白尿及糖尿病视网膜病变的影响因素。采用受试者工作特征曲线(ROC)分析Non-HDL-C/HDL-C对微量白蛋白尿的预测价值。结果 微量白蛋白尿组的年龄、病程、收缩压、舒张压、肌酐、总胆固醇(TC)、三酰甘油(TG)、Non-HDL-C、Non-HDL-C/HDL-C、高血压、使用血管紧张素转化酶抑制剂(ACEI)/血管紧张素Ⅱ受体阻滞剂(ARB)类降压药物的情况均高于无白蛋白尿组,HDL-C低于无白蛋白尿组,差异有统计学意义(P<0.05)。与无视网膜病变组比较,视网膜病变组病程更长,高血压比例更高,差异有统计学意义(P<0.05)。logistic回归分析结果显示,收缩压高、肌酐高、Non-HDL-C/HDL-C大是发生微量白蛋白尿的危险因素(P<0.05)。收缩压、肌酐、Non-HDL-C/HDL-C联合预测微量白蛋白尿的ROC曲线下面积(AUC)为0.774。结论 T2DM患者Non-HDL-C与HDL-C比值大是发生微量白蛋白尿的危险因素,收缩压、肌酐、Non-HDL-C/HDL-C联合检测对微量白蛋白尿有较高的预测价值,值得临床借鉴。

糖尿病,2型  /  高密度脂蛋白胆固醇  /  糖尿病肾病  /  糖尿病视网膜病变

Objective To investigate the correlation between the ratio of serum non-high-density lipoprotein cholesterol/high-density lipoprotein cholesterol (Non-HDL-C/HDL-C) and microalbuminuria in patients with type 2 diabetes mellitus(T2DM). Methods A total of 576 patients with T2DM hospitalized in the Endocrinology Department of the First Affiliated Hospital of Chongqing Medical University from 2017 to 2019 were included in present study (373 males and 203 females, aged 59±11.8 years). The clinical data of patients were collected including: age, height, weight, history of smoking, duration of diabetes, glycosylated hemoglobin (HbA1c), comorbidities, medication status, blood lipid level, etc. The values of non-high-density lipoprotein cholesterol (Non-HDL-C) and the ratio of Non-HDL-C and HDL-C (Non-HDL-C/HDL-C) were then calculated. Depending on whether they have microalbuminuria and diabetic retinopathy, patients were divided into groups for comparative statistical analysis, the binary logistic regression model was used to analyze the influencing factors of microalbuminuria and diabetic retinopathy. Finally, the receiver operating characteristic curve (ROC) was adopted to analyze the predictive value of Non-HDL-C/HDL-C for microalbuminuria. Results The age, course of disease, systolic blood pressure, diastolic blood pressure, creatinine, total cholesterol (TC), triglyceride (TG), Non-HDL-C, Non-HDL-C/HDL-C, hypertension and the use of angiotensin-converting enzyme inhibitor (ACEI)/angiotensin receptor blocker (ARB) hypotensive drugs were all higher, but the HDL-C was lower in microalbuminuria group than those in non-proteinuria group with statistically significant differences (P<0.05). The course of disease longer and the proportion of hypertension is higher in retinopathy group than those in non-nephropathy group, with statistically significant differences (P<0.05). The logistic regression analysis revealed that high systolic blood pressure, high creatinine level, and high Non-HDL-C/HDL-C ratio were the influencing factors for the occurrence of microalbuminuria (P<0.05). The area under the ROC curve (AUC) of combined systolic blood pressure, creatinine level and Non-HDL-C/HDL-C to predict microalbuminuria is 0.774. Conclusions A large Non-HDL-C/HDL-C ratio is a risk factor for occurrence of microalbuminuria in patients with T2DM. Combined detection of systolic blood pressure, creatinine level and Non-HDL-C/HDL-C has a high predictive value for microalbuminuria, so is worthy of clinical reference.

diabetes mellitus, type 2  /  high density lipoprotein cholesterol  /  diabetic nephropathy  /  diabetic retinopathy
钱文杰, 陈月, 王蕴婷, 任子愚, 伍询, 任伟. 2型糖尿病患者血清Non-HDL-C/HDL-C与微量白蛋白尿的相关性. 解放军医学杂志, 2021 , 46 (2) : 169 -174 . DOI: 10.11855/j.issn.0577-7402.2021.02.10
Wen-Jie Qian, Yue Chen, Yun-Ting Wang, Zi-Yu Ren, Xun Wu, Wei Ren. Correlation between serum non-high-density lipoprotein cholesterol/high-density lipoprotein cholesterol and microalbuminuria in patients with type 2 diabetes[J]. Medical Journal of Chinese People’s Liberation Army, 2021 , 46 (2) : 169 -174 . DOI: 10.11855/j.issn.0577-7402.2021.02.10
目前,糖尿病肾病(diabetic nephropathy,DN)是慢性肾脏病的主要原因[1],2017年全球范围内年龄标准化患病率达到男性15.48/1000,女性16.5/1000[2]。DN的自然病史主要包括肾小球超滤、进行性蛋白尿、肾小球滤过率下降以及最终出现终末期肾病[3]。目前,临床上主要通过尿微量白蛋白与肌酐的比值来筛查DN[4],而脂质代谢在DN的发生发展中扮演着重要角色,低密度脂蛋白胆固醇(LDL-C)被认为是心血管病的危险因素,但是越来越多的研究证实,非高密度脂蛋白胆固醇(Non-HDL-C)预测心血管事件优于LDL-C[5]。有研究发现,在慢性肾脏病患者中Non-HDL-C/HDL-C的比值与冠心病的发生相关[6],但Non-HDL-C/HDL-C是否与微量白蛋白尿的发生相关尚无定论。本研究旨在讨论2型糖尿病(T2DM)患者Non-HDL-C/HDL-C与微量白蛋白尿的相关性,从而为早期预防DN提供参考。
本研究为回顾性研究,选取2017—2019年在重庆医科大学附属第一医院内分泌科住院的T2DM的患者共576例,其中男373例,女203例,年龄(59±11.8)岁。根据患者入院两次检查尿微量白蛋白/肌酐比值的结果,以其平均值<30 mg/g作为无白蛋白尿组(n=449),30~300 mg/g作为微量白蛋白尿组(n=127)。其中,528例患者在眼科门诊行眼底照相,依据2002年悉尼国际眼科会议制定的诊断标准[7]分为无视网膜病变组(n=413)和视网膜病变组(n=115)。本研究已经重庆医科大学附属第一医院伦理委员会批准。
所有患者均符合1999 WHO糖尿病诊断标准。排除1型糖尿病、妊娠糖尿病、特殊类型糖尿病,严重感染、手术、急性应激、恶性肿瘤病史、甲状腺功能亢进症、糖尿病酮症酸中毒、高渗高糖综合征、自身免疫性疾病,慢性肾小球肾炎、狼疮肾炎、尿毒症等肾脏疾病,以及长期使用糖皮质激素或免疫抑制剂等患者。
收集患者的临床资料如性别、年龄、身高、体重等。在安静的情况下测量患者血压。吸烟史定义为连续或者累计吸烟≥6个月。询问患者糖尿病病程、是否合并高血压病史、使用降脂药物情况、使用血管紧张素转化酶抑制剂(ACEI)/血管紧张素Ⅱ受体阻滞剂(ARB)类降压药物情况。
所有患者于空腹状态下抽取静脉血,采用Alcyon300生化仪测定血脂,高效液相色谱法测定糖化血红蛋白(HbA1c),全自动生化仪测定尿微量白蛋白/肌酐比值两次。通过计算得到Non-HDL-C、Non-HDL-C/HDL-C水平。计算公式为:Non-HDL-C=TC–HDL-C;Non-HDL-C/HDLC=(TC–HDL-C)/HDL-C。
根据患者在重庆医科大学附属第一医院眼科门诊行眼底照相或眼底荧光造影的结果,以2002年悉尼眼科国际会议标准诊断[6]视网膜病变。两次尿微量白蛋白/肌酐的平均值在30~300 mg/g可诊断为糖尿病微量白蛋白尿患者,并除外其他疾病导致的尿白蛋白升高。
比较不同分组患者的一般资料,筛选出微量白蛋白尿、视网膜病变的危险因素,并分别与微量白蛋白尿、视网膜病变行相关性分析。构建T2DM患者微量白蛋白尿发生的预测模型,并分析其预测微量白蛋白尿的效能。
采用SPSS 22.0软件进行分析。经Shapiro-Wilk检验,数据均为非正态性分布,故计量资料以M(Q1Q3)表示,组间比较采用非参数秩和检验;采用二元logistic回归分析微量白蛋白尿发生的影响因素;采用受试者工作特征曲线(ROC)分析Non-HDL-C/HDL-C对微量白蛋白尿的预测价值。P<0.05为差异有统计学意义。
微量白蛋白尿组的年龄、病程、收缩压、舒张压、肌酐、TC、TG、Non-HDL-C、Non-HDL-C/HDL-C、高血压、使用ACEI/ARB类降压药物的情况均明显高于无白蛋白尿组,而HDL-C低于无白蛋白尿组,差异有统计学意义(P<0.05);两组的性别、身高、体重、吸烟史、HbA1c、尿素、LDL-C、使用降脂药物情况差异无统计学意义(P>0.05,表1)。
与无视网膜病变组比较,视网膜病变组病程更长、高血压比例更高,差异有统计学意义(P<0.05)。但两组的性别、年龄、身高、体重、吸烟史、HbA1c、收缩压、舒张压、尿素、肌酐、TC、TG、HDL-C、LDL-C、Non-HDL-C、Non-HDL-C/HDL-C、服用降脂药物情况、使用ACEI/ARB类降压药物情况差异无统计学意义(P>0.05,表2)。
以是否有微量白蛋白尿为因变量(有=1,无=0),经过共线性诊断,以年龄、病程、收缩压、舒张压、肌酐、TG、Non-HDL-C、Non-HDL-C/HDL-C、高血压、使用ACEI/ARB类降压药物情况为自变量进行二元logistic回归分析,结果显示,收缩压、肌酐、Non-HDL-C/HDL-C是微量白蛋白尿发生的影响因素(P<0.05,表3)。
表3中所得到的微量白蛋白尿的影响因素进行预测价值分析,结果显示,收缩压、肌酐、Non-HDL-C/HDL-C联合预测微量白蛋白尿的ROC曲线下面积(AUC)为0.774(图1)。
Oellgaard等[8]发现,对糖尿病微量白蛋白尿患者进行早期、多重积极干预可以延缓DN的进展,降低终末期肾病的发生风险。因此,早期筛查、早期诊断、早期治疗微量白蛋白尿对改善T2DM患者肾脏疾病进展有重要临床意义。血Non-HDL-C为除了HDL-C的其他脂蛋白胆固醇的总和,可更好地反映血浆中致动脉粥样硬化颗粒的总和。2011年欧洲心脏病学会(ESC)/欧洲动脉粥样硬化学会(EAS)血脂异常管理指南以及2016年修订版中国成人血脂异常防治指南推荐使用Non-HDL-C作为干预靶点[9-10];同时,国外一项1970—2013年的随访研究显示,随着Non-HDL-C水平的增高,研究人群的心血管疾病发生率逐年升高[11]。而多项研究发现,Non-HDL-C/HDL-C在预测心血管事件中优于Non-HDL-C,并与apoB/apoA1相似,不过apoB/apoA1还可以很好地预测胰岛素抵抗及代谢综合征[12-17]。本研究发现,在T2DM患者中,微量白蛋白尿患者的TC、TG、Non-HDL-C、Non-HDL-C/HDL-C明显升高,而HDL-C明显降低,且T2DM患者Non-HDL-C/HDL-C越大,越有可能出现微量白蛋白尿。Yang等[18]发现,Non-HDL-C/HDL-C越高,越容易合并DN;Chang等[19]的研究发现,微量白蛋白尿的发生与低HDL-C相关,均与本研究结果一致。Non-HDL-C/HDL-C升高影响微量白蛋白尿发生发展的机制可能为:(1)Non-HDL-C/HDL-C升高可抑制胆固醇的逆转运;(2)通过酪氨酸激酶依赖性及蛋白激酶C依赖性机制,使肾脏系膜细胞及基质增生;(3)通过影响对氧磷酶1的活性,增强氧化应激,促进蛋白尿的生成。
糖尿病视网膜病变也是困扰糖尿病患者的严重微血管并发症之一。临床关于血脂相关指标与糖尿病视网膜病变关系的多项研究结论并不一致。本研究发现,视网膜病变组的病程及高血压比例均高于无视网膜病变组,而血脂相关指标与糖尿病视网膜病变无明显相关性。但Zhang等[20]纳入523例糖尿病患者的研究发现,较高的总胆固醇浓度、VLDL-C及三酰甘油与糖尿病视网膜病变的风险增加有关。此外,有研究发现,给予糖尿病视网膜病变患者以非诺贝特干预可降低患者对激光治疗的需求,延缓视网膜病变的进展[21-22]。这些研究与本研究结果不符。而Xu等[23]的研究纳入北京2007例糖尿病患者发现,糖尿病患者视网膜病变与年龄小、病程长、HbA1c高、收缩压高、体重指数低、血尿素浓度高相关,而与血脂指标无相关性。同时,Elwali等[24]的研究也表明,胆固醇、三酰甘油不是糖尿病视网膜病变的危险因素。考虑本研究由于缺少患者的降糖方案、降糖治疗的依从性、其他糖尿病并发症等数据资料,因此不能消除相关的偏倚。
本研究行logistic回归分析发现,收缩压、肌酐、Non-HDL-C/HDL-C是微量白蛋白尿发生的影响因素;以收缩压、肌酐、Non-HDL-C/HDL-C构建的回归模型进行ROC曲线分析,结果显示,收缩压、肌酐、Non-HDL-C/HDL-C联合预测微量白蛋白尿的AUC为0.774,联合诊断微量白蛋白尿有较高的诊断效能,对临床有一定的指导意义。
本研究存在一定的局限性:(1)为横断面研究,无法阐述危险因素与结果之间的因果关系,且样本量较小,仍需更大样本的队列或前瞻性研究进行验证;(2)缺少患者的降糖方案、降糖治疗的依从性、其他糖尿病并发症等数据资料,未能校正所有的混杂因素;(3)微量白蛋白尿预测DN有一定局限性,需结合肾小球滤过率的长期多次随访结果,必要时需结合糖尿病视网膜病变甚至是肾穿刺病理活检进行诊断。
综上所述,T2DM患者Non-HDL-C与HDL-C比值增大是微量白蛋白尿发生的危险因素,收缩压、肌酐、Non-HDL-C/HDL-C联合构建的回归模型可提高早期预测微量白蛋白尿的准确性,从而为早期预防DN提供参考。
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2021年第46卷第2期
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doi: 10.11855/j.issn.0577-7402.2021.02.10
  • 接收时间:2020-09-27
  • 首发时间:2025-12-26
  • 出版时间:2021-02-28
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  • 收稿日期:2020-09-27
  • 修回日期:2020-12-23
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    重庆医科大学附属第一医院内分泌科,重庆 400010

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2种不同金属材料的力学参数

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属数
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genus
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species
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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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