Article(id=1241034441997480227, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241034441380917539, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202411233, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1731427200000, receivedDateStr=2024-11-13, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773815268277, onlineDateStr=2026-03-18, pubDate=1749484800000, pubDateStr=2025-06-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773815268277, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773815268277, creator=13701087609, updateTime=1773815268277, updator=13701087609, issue=Issue{id=1241034441380917539, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='11', pageStart='1921', pageEnd='2112', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773815268130, creator=13701087609, updateTime=1773815340947, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241034746873049765, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241034441380917539, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241034746873049766, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241034441380917539, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1921, endPage=1927, ext={EN=ArticleExt(id=1241034442316247333, articleId=1241034441997480227, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Study on the association and predictive value of early pregnancy blood routine indicators and fasting blood glucose levels with gestational diabetes mellitus, columnId=1240413921954295836, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods, runingTitle=null, highlight=null, articleAbstract=
Objective

To investigate the association between early pregnancy blood routine indicators and fasting blood glucose (FPG) levels with gestational diabetes mellitus (GDM) and their predictive value.

Methods

A total of 1 422 early pregnant women enrolled in a prospective dynamic birth cohort at Gansu Provincial Maternal and Child Health Hospital from 2018 to 2022 were included. Baseline data and early pregnancy laboratory indicators were collected, and GDM occurrence was followed up and recorded. Logistic regression was used to analyze the relationship between early pregnancy white blood cell count (WBC),lymphocyte count (LYMPH), hemoglobin (HGB), and FPG levels with the confirmed GDM outcome. Restricted cubic spline (RCS) analysis was conducted to investigate whether there was a nonlinear relationship between WBC, FPG, and GDM. Additionally, subgroups were analyzed based on age, parity, and other factors. Finally, the predictive value of various early pregnancy indicators for GDM was assessed using receiver operating characteristic (ROC) curves.

Results

Among the 1 422 early pregnant women, 154 developed GDM in mid-pregnancy. After adjusting for covariates such as age, pre-pregnancy BMI, and parity, logistic regression analysis revealed that the risk of developing GDM for the highest quartile levels of WBC, LYMPH, HGB, and FPG was 1.774 times (95%CI: 1.088-2.893), 1.712 times (95%CI: 1.035-2.833), 1.597 times (95%CI: 1.004-2.555), and 6.459 times (95%CI: 3.612-11.151) that of the lowest quartile group, respectively, with all differences being statistically significant (P<0.05). RCS analysis indicated a positive linear dose-response relationship between early pregnancy WBC, FPG, and the risk of GDM. In subgroup analysis, overweight and obese women showed an increased risk of GDM with elevated early pregnancy WBC (OR=1.212,95%CI: 1.106-1.445) and FPG (OR=6.758, 95%CI: 3.407-14.989). The combination of early pregnancy WBC, FPG, HGB, age, and pre-pregnancy BMI provided the best predictive value for GDM (AUC=0.736, 95%CI: 0.695-0.776).

Conclusion

Clinical practitioners should focus on early pregnancy WBC and FPG levels, as well as the conditions of advanced maternal age and overweight/obesity, to implement timely health interventions for primary prevention of GDM.

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目的

探讨孕早期血常规指标和空腹血糖(FPG)与妊娠糖尿病(GDM)之间的关联及其预测价值。

方法

纳入2018—2022年加入甘肃省妇幼保健院前瞻性动态出生人口队列的孕早期孕妇,收集其基线资料和孕早期检化验指标,随访并记录GDM发生状况。使用logistic回归分析孕早期白细胞计数(WBC)、淋巴细胞计数(LYMPH)、血红蛋白(HGB)等指标和FPG水平与确诊GDM结局之间的关系,运用限制性立方样条图(RCS)分析WBC、FPG与GDM是否存在非线性关系,同时根据年龄、孕次、产次等将研究对象分为不同亚组开展分析。最后运用受试者工作特征曲线(ROC)分析孕早期各类指标对GDM的预测价值。

结果

共纳入1 422名孕早期孕妇,有154名孕妇孕中期罹患GDM。在调整年龄、孕前BMI、孕次等协变量后,logistic回归分析发现:WBC、LYMPH、HGB、FPG最高四分位数水平(Q4)组发生GDM的风险分别为最低四分位数(Q1)组的1.774(95%CI:1.088~2.893)、1.712(95%CI:1.035~2.833)、1.597(95%CI:1.004~2.555)和6.459倍(95%CI:3.612~11.151),差异均有统计学意义(P<0.05)。RCS显示,孕早期WBC、FPG与GDM发病风险存在正向的线性剂量—反应关系。亚组分析中超重和肥胖组孕妇,孕早期WBC(OR=1.212,95%CI:1.106~1.445)和FPG(OR=6.758,95%CI:3.407~14.989)升高,GDM发病风险增加。孕早期WBC联合FPG、HGB、年龄和孕前BMI对GDM预测价值最好(AUC=0.736,95%CI:0.695~0.776)。

结论

临床医护应重点关注孕妇孕早期WBC、FPG水平,以及高龄和超重肥胖状况,及时实施健康干预,以实现GDM的一级预防,防患于未然。

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王燕侠,E-mail:
, copyrightStatement=本刊刊出的所有文章不代表中华预防医学会和本刊编委会的观点,除非特别声明。, copyrightOwner=中华预防医学会和四川大学华西公共卫生学院, extLink=null, articleAbsUrl=null, sourceXml=BMYMjBkDfDYStA1yBxiiDA==, magXml=k5muMDlJmm5HsK3SJTRWDw==, pdfUrl=null, pdf=pMaaLjJ1mY6bcNoLlkTAeA==, pdfFileSize=938134, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=M/rlDIp5YrCoPDd4R+gzCg==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=m+V5wYT8ixJuhpXjvxRFNQ==, mapNumber=null, authorCompany=null, fund=null, authors=

刘旭辉(1999—),男,硕士在读,研究方向:妇幼保健

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刘旭辉(1999—),男,硕士在读,研究方向:妇幼保健

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Medicine, 2022, 101(36): e30514., articleTitle=A new marker predicting gestational diabetes mellitus: First trimester neutrophil/lymphocyte ratio, refAbstract=null)], funds=[Fund(id=1241050838454555077, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034441997480227, awardId=2016YFC1000100, language=CN, fundingSource=国家重点计划专项(2016YFC1000100), fundOrder=null, country=null), Fund(id=1241050838576189897, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034441997480227, awardId=22JR11RA177, language=CN, fundingSource=甘肃省自然科学基金(22JR11RA177), fundOrder=null, country=null), Fund(id=1241050838773322194, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034441997480227, awardId=24JRRA938, language=CN, fundingSource=甘肃省联合科研基金一般项目(24JRRA938), fundOrder=null, country=null), Fund(id=1241050839171781076, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034441997480227, awardId=NCRCCHD-2022-GP-17, language=CN, fundingSource=国家儿童健康与疾病临床医学研究中心临床医学研究一般项目(NCRCCHD-2022-GP-17), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241050823476695935, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034441997480227, xref=1., ext=[AuthorCompanyExt(id=1241050823480890240, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034441997480227, companyId=1241050823476695935, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=School of Public Health, Gansu University of Chinese Medicine, Lanzhou, Gansu 730101, China), AuthorCompanyExt(id=1241050823493473153, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034441997480227, companyId=1241050823476695935, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.甘肃中医药大学公共卫生学院,甘肃 兰州 730101)]), AuthorCompany(id=1241050823661245319, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034441997480227, xref=2., ext=[AuthorCompanyExt(id=1241050823669633929, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034441997480227, companyId=1241050823661245319, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.甘肃省妇幼保健院科研中心,甘肃 兰州 730050)])], figs=[ArticleFig(id=1241050835422073174, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034441997480227, language=EN, label=Figure 1, caption=Relationship between WBC and FPG levels in early pregnancy and risk of developing GDM, figureFileSmall=EonQklTofGu/fT9EmwNRRg==, figureFileBig=UoXL7eMvlxHlTmr43yem6g==, tableContent=null), ArticleFig(id=1241050835904418137, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034441997480227, language=CN, label=图1, caption=孕早期WBC、FPG水平和GDM发病风险之间的关系

注:A为孕早期WBC与GDM发病风险之间的关系;B为孕早期FPG与GDM发病风险之间的关系。

, figureFileSmall=EonQklTofGu/fT9EmwNRRg==, figureFileBig=UoXL7eMvlxHlTmr43yem6g==, tableContent=null), ArticleFig(id=1241050836105744745, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034441997480227, language=EN, label=Figure 2, caption=ROC curve for prediction of GDM by WBC,FPG and HGB combined with clinical features in early pregnancy, figureFileSmall=N8uIrVYmCzQU3wvwbqcL2g==, figureFileBig=Z0ZBmoEFmJLfwyJ3zlnikQ==, tableContent=null), ArticleFig(id=1241050836202213742, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034441997480227, language=CN, label=图2, caption=孕早期FPG、WBC、HGB结合临床特征预测GDM的ROC曲线, figureFileSmall=N8uIrVYmCzQU3wvwbqcL2g==, figureFileBig=Z0ZBmoEFmJLfwyJ3zlnikQ==, tableContent=null), ArticleFig(id=1241050836378374520, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034441997480227, language=EN, label=Table 1, caption=

Baseline characteristics of the GDM group and non-GDM group [(),n(%),MP25P75)]

, figureFileSmall=null, figureFileBig=null, tableContent=
变量总体(n=1 422)GDM组(n=154)非GDM组(n=1 268)t/χ2/ZP
年龄(岁)30.23±3.7631.51±4.2830.07±3.65-4.524<0.001
年龄(岁)9.1750.002
<351 207(84.88)118(76.62)1 089(85.88)
≥35215(15.12)36(23.38)179(14.12)
民族0.123*
汉族1 321(92.90)149(96.75)1 172(92.43)
回族51(3.59)2(1.30)49(3.86)
藏族27(1.90)3(1.95)24(1.89)
其他23(1.61)0(0.00)23(1.81)
孕前BMI(kg/m2)21.80±4.4922.81±4.5221.68±4.48-2.9340.003
孕前BMI(kg/m2)10.3850.006
消瘦228(16.03)16(10.39)212(16.72)
正常920(64.70)95(61.69)825(65.06)
超重及肥胖274(19.27)43(27.92)231(18.22)
文化程度5.3810.146
初中及以下72(5.06)2(1.30)70(5.52)
高中/中专168(11.81)20(12.99)148(11.67)
大专/本科1 052(73.98)119(77.27)933(73.58)
研究生及以上130(9.15)13(8.44)117(9.23)
主动吸烟0.719*
21(1.48)1(0.65)20(1.58)
1 401(98.52)153(99.35)1 248(98.42)
被动吸烟1.8970.168
784(55.13)77(50.00)707(49.72)
638(44.87)77(50.00)561(44.15)
饮酒0.0620.803
46(3.23)6(3.90)40(3.15)
1 376(96.77)148(96.10)1 228(96.85)
孕次(次)0.0220.881
1839(59.00)90(58.44)749(59.07)
≥2583(41.00)64(41.56)519(40.93)
产次(次)0.0190.891
1958(67.37)103(66.88)855(67.43)
≥2464(32.63)51(33.12)413(32.57)
受孕方式0.1430.705
自然怀孕1 374(96.62)148(96.10)1 226(96.69)
辅助怀孕48(3.38)6(3.90)42(3.31)
RBC(×1012/L)4.40(4.10,4.60)4.40(4.20,4.70)4.40(4.10,4.60)-1.5510.121
WBC(×109/L)7.70(6.60,9.03)8.23(6.97,9.77)7.67(6.57,8.94)-3.0780.002
NEUT(×109/L)5.76(4.77,6.81)6.11(5.10,7.34)5.73(4.76,6.77)-2.3270.020
LYMPH(×109/L)1.53(1.27,1.53)1.61(1.35,1.94)1.52(1.25,1.83)-2.5200.012
PLT(×109/L)209.00(175.00,248.00)212.50(178.75,251.25)209.00(173.00,248.00)-1.0300.303
HGB(g/L)133.00(126.00,139.00)135.50(127.75,141.25)133.00(126.00,139.00)-2.6780.007
PCV(%)39.1(37.40,41.00)39.45(37.60,41.43)39.10(37.33,40.90)-1.6910.091
FPG(mmol/L)4.51(4.27,4.73)4.73(4.49,4.99)4.48(4.24,4.70)-7.784<0.001
), ArticleFig(id=1241050836680364414, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034441997480227, language=CN, label=表1, caption=

GDM组和非GDM组基线特征比较[(),n(%),MP25P75)]

, figureFileSmall=null, figureFileBig=null, tableContent=
变量总体(n=1 422)GDM组(n=154)非GDM组(n=1 268)t/χ2/ZP
年龄(岁)30.23±3.7631.51±4.2830.07±3.65-4.524<0.001
年龄(岁)9.1750.002
<351 207(84.88)118(76.62)1 089(85.88)
≥35215(15.12)36(23.38)179(14.12)
民族0.123*
汉族1 321(92.90)149(96.75)1 172(92.43)
回族51(3.59)2(1.30)49(3.86)
藏族27(1.90)3(1.95)24(1.89)
其他23(1.61)0(0.00)23(1.81)
孕前BMI(kg/m2)21.80±4.4922.81±4.5221.68±4.48-2.9340.003
孕前BMI(kg/m2)10.3850.006
消瘦228(16.03)16(10.39)212(16.72)
正常920(64.70)95(61.69)825(65.06)
超重及肥胖274(19.27)43(27.92)231(18.22)
文化程度5.3810.146
初中及以下72(5.06)2(1.30)70(5.52)
高中/中专168(11.81)20(12.99)148(11.67)
大专/本科1 052(73.98)119(77.27)933(73.58)
研究生及以上130(9.15)13(8.44)117(9.23)
主动吸烟0.719*
21(1.48)1(0.65)20(1.58)
1 401(98.52)153(99.35)1 248(98.42)
被动吸烟1.8970.168
784(55.13)77(50.00)707(49.72)
638(44.87)77(50.00)561(44.15)
饮酒0.0620.803
46(3.23)6(3.90)40(3.15)
1 376(96.77)148(96.10)1 228(96.85)
孕次(次)0.0220.881
1839(59.00)90(58.44)749(59.07)
≥2583(41.00)64(41.56)519(40.93)
产次(次)0.0190.891
1958(67.37)103(66.88)855(67.43)
≥2464(32.63)51(33.12)413(32.57)
受孕方式0.1430.705
自然怀孕1 374(96.62)148(96.10)1 226(96.69)
辅助怀孕48(3.38)6(3.90)42(3.31)
RBC(×1012/L)4.40(4.10,4.60)4.40(4.20,4.70)4.40(4.10,4.60)-1.5510.121
WBC(×109/L)7.70(6.60,9.03)8.23(6.97,9.77)7.67(6.57,8.94)-3.0780.002
NEUT(×109/L)5.76(4.77,6.81)6.11(5.10,7.34)5.73(4.76,6.77)-2.3270.020
LYMPH(×109/L)1.53(1.27,1.53)1.61(1.35,1.94)1.52(1.25,1.83)-2.5200.012
PLT(×109/L)209.00(175.00,248.00)212.50(178.75,251.25)209.00(173.00,248.00)-1.0300.303
HGB(g/L)133.00(126.00,139.00)135.50(127.75,141.25)133.00(126.00,139.00)-2.6780.007
PCV(%)39.1(37.40,41.00)39.45(37.60,41.43)39.10(37.33,40.90)-1.6910.091
FPG(mmol/L)4.51(4.27,4.73)4.73(4.49,4.99)4.48(4.24,4.70)-7.784<0.001
), ArticleFig(id=1241050836915245450, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034441997480227, language=EN, label=Table 2, caption=

Association between routine blood indicators, FPG and the development of GDM in early pregnancy

, figureFileSmall=null, figureFileBig=null, tableContent=
变量名模型1模型2
OR值(95%CI)POR值(95%CI)P
WBC(×109/L)
Q1(<6.60)Ref.Ref.
Q2(6.60~7.70)1.022(0.602~1.734)0.9360.979(0.573~1.673)0.939
Q3(7.71~9.07)1.485(0.911~2.420)0.1131.363(0.831~2.238)0.220
Q4(≥9.08)1.878(1.163~3.033)0.0101.774(1.088~2.893)0.021
NEUT(×109/L)
Q1(<4.77)Ref.Ref.
Q2(4.77~5.75)1.192(0.667~2.129)0.5531.158(0.642~2.086)0.626
Q3(5.76~6.80)1.223(0.684~2.185)0.4971.132(0.627~2.042)0.682
Q4(≥6.81)1.666(0.959~2.892)0.0701.502(0.853~2.654)0.159
LYMPH(×109/L)
Q1(<1.27)Ref.Ref.
Q2(1.27~1.52)1.367(0.817~2.286)0.2341.334(0.793~2.245)0.278
Q3(1.53~1.83)1.650(0.997~2.728)0.0511.598(0.959~2.665)0.072
Q4(≥1.84)1.758(1.074~2.878)0.0251.712(1.035~2.833)0.036
HGB(g/L)
Q1(<126)Ref.Ref.
Q2(126-132)0.875(0.517~1.481)0.6200.843(0.495~1.437)0.531
Q3(133-138)1.160(0.704~1.910)0.5601.075(0.647~1.785)0.780
Q4(≥139)1.654(1.043~2.623)0.0331.597(1.004~2.555)0.049
FPG(mmol/L)
Q1(<4.27)Ref.Ref.
Q2(4.27~4.50)1.707(0.884~3.296)0.111.657(0.853~3.218)0.136
Q3(4.51~4.72)2.346(1.250~4.402)0.0082.206(1.168~4.166)0.015
Q4(≥4.73)6.363(3.588~11.285)<0.0016.459(3.612~11.151)<0.001
), ArticleFig(id=1241050837221429648, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034441997480227, language=CN, label=表2, caption=

孕早期血常规指标、FPG与GDM的发病关联

, figureFileSmall=null, figureFileBig=null, tableContent=
变量名模型1模型2
OR值(95%CI)POR值(95%CI)P
WBC(×109/L)
Q1(<6.60)Ref.Ref.
Q2(6.60~7.70)1.022(0.602~1.734)0.9360.979(0.573~1.673)0.939
Q3(7.71~9.07)1.485(0.911~2.420)0.1131.363(0.831~2.238)0.220
Q4(≥9.08)1.878(1.163~3.033)0.0101.774(1.088~2.893)0.021
NEUT(×109/L)
Q1(<4.77)Ref.Ref.
Q2(4.77~5.75)1.192(0.667~2.129)0.5531.158(0.642~2.086)0.626
Q3(5.76~6.80)1.223(0.684~2.185)0.4971.132(0.627~2.042)0.682
Q4(≥6.81)1.666(0.959~2.892)0.0701.502(0.853~2.654)0.159
LYMPH(×109/L)
Q1(<1.27)Ref.Ref.
Q2(1.27~1.52)1.367(0.817~2.286)0.2341.334(0.793~2.245)0.278
Q3(1.53~1.83)1.650(0.997~2.728)0.0511.598(0.959~2.665)0.072
Q4(≥1.84)1.758(1.074~2.878)0.0251.712(1.035~2.833)0.036
HGB(g/L)
Q1(<126)Ref.Ref.
Q2(126-132)0.875(0.517~1.481)0.6200.843(0.495~1.437)0.531
Q3(133-138)1.160(0.704~1.910)0.5601.075(0.647~1.785)0.780
Q4(≥139)1.654(1.043~2.623)0.0331.597(1.004~2.555)0.049
FPG(mmol/L)
Q1(<4.27)Ref.Ref.
Q2(4.27~4.50)1.707(0.884~3.296)0.111.657(0.853~3.218)0.136
Q3(4.51~4.72)2.346(1.250~4.402)0.0082.206(1.168~4.166)0.015
Q4(≥4.73)6.363(3.588~11.285)<0.0016.459(3.612~11.151)<0.001
), ArticleFig(id=1241050837498253716, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034441997480227, language=EN, label=Table 3, caption=

Multifactorial analysis of the association between routine blood indicators, FPG and the development of GDM in early pregnancy

, figureFileSmall=null, figureFileBig=null, tableContent=
变量名β标准误差Wald χ2OR95%CIP
WBC0.1560.0577.4211.1681.045~1.3070.006
LYMPH0.0300.2190.0191.0300.671~1.5830.891
HGB0.0130.0091.8881.0130.995~1.0310.169
FPG1.5790.21752.7554.8493.167~7.424<0.001
), ArticleFig(id=1241050837724746145, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034441997480227, language=CN, label=表3, caption=

孕早期血常规指标、FPG与GDM发病关联的多因素分析

, figureFileSmall=null, figureFileBig=null, tableContent=
变量名β标准误差Wald χ2OR95%CIP
WBC0.1560.0577.4211.1681.045~1.3070.006
LYMPH0.0300.2190.0191.0300.671~1.5830.891
HGB0.0130.0091.8881.0130.995~1.0310.169
FPG1.5790.21752.7554.8493.167~7.424<0.001
), ArticleFig(id=1241050837867352490, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034441997480227, language=EN, label=Table 4, caption=

Subgroup analysis of the association between WBC and FPG levels in early pregnancy and the development of GDM

, figureFileSmall=null, figureFileBig=null, tableContent=
亚组WBCFPG
OR值(95%CI)POR值(95%CI)P
年龄(岁)
<351.165(1.022~1.329)0.0224.781(2.602~8.785)<0.001
≥351.255(1.103~1.427)0.0014.766(2.689~8.445)<0.001
孕前BMI
消瘦1.120(0.850~1.474)0.4204.904(1.374~17.527)0.014
正常1.179(1.050~1.324)0.0063.597(2.199~6.108)<0.001
超重和肥胖1.212(1.106~1.445)0.0326.758(3.407~14.989)<0.001
孕次(次)
11.199(1.067~1.346)0.0024.708(2.694~8.299)<0.001
≥21.177(1.018~1.361)0.0284.719(2.553~8.724)<0.001
产次(次)
1次1.196(1.073~1.334)0.0014.839(2.847~8.225)<0.001
≥2次1.175(0.999~1.382)0.0524.516(2.341~8.712)<0.001
), ArticleFig(id=1241050838056096178, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241034441997480227, language=CN, label=表4, caption=

亚组分析孕早期WBC、FPG水平和GDM发病关联

, figureFileSmall=null, figureFileBig=null, tableContent=
亚组WBCFPG
OR值(95%CI)POR值(95%CI)P
年龄(岁)
<351.165(1.022~1.329)0.0224.781(2.602~8.785)<0.001
≥351.255(1.103~1.427)0.0014.766(2.689~8.445)<0.001
孕前BMI
消瘦1.120(0.850~1.474)0.4204.904(1.374~17.527)0.014
正常1.179(1.050~1.324)0.0063.597(2.199~6.108)<0.001
超重和肥胖1.212(1.106~1.445)0.0326.758(3.407~14.989)<0.001
孕次(次)
11.199(1.067~1.346)0.0024.708(2.694~8.299)<0.001
≥21.177(1.018~1.361)0.0284.719(2.553~8.724)<0.001
产次(次)
1次1.196(1.073~1.334)0.0014.839(2.847~8.225)<0.001
≥2次1.175(0.999~1.382)0.0524.516(2.341~8.712)<0.001
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孕早期血常规指标、空腹血糖水平与妊娠期糖尿病的关联及预测价值研究
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刘旭辉 1 , 张玲 2 , 李斐 1 , 郭志茹 1 , 刘一宁 1 , 易彬 2 , 王文玲 2 , 金玉霞 2 , 王燕侠 2
现代预防医学 | 流行病与统计方法 2025,52(11): 1921-1927
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现代预防医学 | 流行病与统计方法 2025, 52(11): 1921-1927
孕早期血常规指标、空腹血糖水平与妊娠期糖尿病的关联及预测价值研究
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刘旭辉1, 张玲2, 李斐1, 郭志茹1, 刘一宁1, 易彬2, 王文玲2, 金玉霞2, 王燕侠2
作者信息
  • 1.甘肃中医药大学公共卫生学院,甘肃 兰州 730101
  • 2.甘肃省妇幼保健院科研中心,甘肃 兰州 730050
  • 刘旭辉(1999—),男,硕士在读,研究方向:妇幼保健

通讯作者:

王燕侠,E-mail:
Study on the association and predictive value of early pregnancy blood routine indicators and fasting blood glucose levels with gestational diabetes mellitus
Xu-hui LIU1, Ling ZHANG2, Fei LI1, Zhi-ru GUO1, Yi-ning LIU1, Bin YI2, Wen-ling WANG2, Yu-xia JIN2, Yan-xia WANG2
Affiliations
  • School of Public Health, Gansu University of Chinese Medicine, Lanzhou, Gansu 730101, China
出版时间: 2025-06-10 doi: 10.20043/j.cnki.MPM.202411233
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目的

探讨孕早期血常规指标和空腹血糖(FPG)与妊娠糖尿病(GDM)之间的关联及其预测价值。

方法

纳入2018—2022年加入甘肃省妇幼保健院前瞻性动态出生人口队列的孕早期孕妇,收集其基线资料和孕早期检化验指标,随访并记录GDM发生状况。使用logistic回归分析孕早期白细胞计数(WBC)、淋巴细胞计数(LYMPH)、血红蛋白(HGB)等指标和FPG水平与确诊GDM结局之间的关系,运用限制性立方样条图(RCS)分析WBC、FPG与GDM是否存在非线性关系,同时根据年龄、孕次、产次等将研究对象分为不同亚组开展分析。最后运用受试者工作特征曲线(ROC)分析孕早期各类指标对GDM的预测价值。

结果

共纳入1 422名孕早期孕妇,有154名孕妇孕中期罹患GDM。在调整年龄、孕前BMI、孕次等协变量后,logistic回归分析发现:WBC、LYMPH、HGB、FPG最高四分位数水平(Q4)组发生GDM的风险分别为最低四分位数(Q1)组的1.774(95%CI:1.088~2.893)、1.712(95%CI:1.035~2.833)、1.597(95%CI:1.004~2.555)和6.459倍(95%CI:3.612~11.151),差异均有统计学意义(P<0.05)。RCS显示,孕早期WBC、FPG与GDM发病风险存在正向的线性剂量—反应关系。亚组分析中超重和肥胖组孕妇,孕早期WBC(OR=1.212,95%CI:1.106~1.445)和FPG(OR=6.758,95%CI:3.407~14.989)升高,GDM发病风险增加。孕早期WBC联合FPG、HGB、年龄和孕前BMI对GDM预测价值最好(AUC=0.736,95%CI:0.695~0.776)。

结论

临床医护应重点关注孕妇孕早期WBC、FPG水平,以及高龄和超重肥胖状况,及时实施健康干预,以实现GDM的一级预防,防患于未然。

妊娠糖尿病  /  孕早期  /  血常规  /  空腹血糖  /  队列研究
Objective

To investigate the association between early pregnancy blood routine indicators and fasting blood glucose (FPG) levels with gestational diabetes mellitus (GDM) and their predictive value.

Methods

A total of 1 422 early pregnant women enrolled in a prospective dynamic birth cohort at Gansu Provincial Maternal and Child Health Hospital from 2018 to 2022 were included. Baseline data and early pregnancy laboratory indicators were collected, and GDM occurrence was followed up and recorded. Logistic regression was used to analyze the relationship between early pregnancy white blood cell count (WBC),lymphocyte count (LYMPH), hemoglobin (HGB), and FPG levels with the confirmed GDM outcome. Restricted cubic spline (RCS) analysis was conducted to investigate whether there was a nonlinear relationship between WBC, FPG, and GDM. Additionally, subgroups were analyzed based on age, parity, and other factors. Finally, the predictive value of various early pregnancy indicators for GDM was assessed using receiver operating characteristic (ROC) curves.

Results

Among the 1 422 early pregnant women, 154 developed GDM in mid-pregnancy. After adjusting for covariates such as age, pre-pregnancy BMI, and parity, logistic regression analysis revealed that the risk of developing GDM for the highest quartile levels of WBC, LYMPH, HGB, and FPG was 1.774 times (95%CI: 1.088-2.893), 1.712 times (95%CI: 1.035-2.833), 1.597 times (95%CI: 1.004-2.555), and 6.459 times (95%CI: 3.612-11.151) that of the lowest quartile group, respectively, with all differences being statistically significant (P<0.05). RCS analysis indicated a positive linear dose-response relationship between early pregnancy WBC, FPG, and the risk of GDM. In subgroup analysis, overweight and obese women showed an increased risk of GDM with elevated early pregnancy WBC (OR=1.212,95%CI: 1.106-1.445) and FPG (OR=6.758, 95%CI: 3.407-14.989). The combination of early pregnancy WBC, FPG, HGB, age, and pre-pregnancy BMI provided the best predictive value for GDM (AUC=0.736, 95%CI: 0.695-0.776).

Conclusion

Clinical practitioners should focus on early pregnancy WBC and FPG levels, as well as the conditions of advanced maternal age and overweight/obesity, to implement timely health interventions for primary prevention of GDM.

Gestational diabetes mellitus  /  Early pregnancy  /  Blood routine  /  White blood cell count  /  Fasting blood glucose  /  Cohort study
刘旭辉, 张玲, 李斐, 郭志茹, 刘一宁, 易彬, 王文玲, 金玉霞, 王燕侠. 孕早期血常规指标、空腹血糖水平与妊娠期糖尿病的关联及预测价值研究. 现代预防医学, 2025 , 52 (11) : 1921 -1927 . DOI: 10.20043/j.cnki.MPM.202411233
Xu-hui LIU, Ling ZHANG, Fei LI, Zhi-ru GUO, Yi-ning LIU, Bin YI, Wen-ling WANG, Yu-xia JIN, Yan-xia WANG. Study on the association and predictive value of early pregnancy blood routine indicators and fasting blood glucose levels with gestational diabetes mellitus[J]. Modern Preventive Medicine, 2025 , 52 (11) : 1921 -1927 . DOI: 10.20043/j.cnki.MPM.202411233
妊娠糖尿病(gestational diabetes mellitus,GDM)是指妊娠期间出现或首次诊断为糖代谢异常,是妊娠期最常见的并发症之一[1]。据统计,我国GDM发病率约为14.8%,高于世界平均水平的13.2%[2-3]。既往研究证实,GDM短期内可增加孕妇和新生儿的不良妊娠结局的发生风险[4-7],还可致使母亲和子代远期罹患糖尿病和心血管疾病的风险上升[8]。因此开展GDM患者的早期诊断和三级预防尤为重要。
孕早期血常规和空腹血糖(fasting plasma glucose,FPG)检查均为我国指南所提倡常规检查之一[9],通过孕早期生化指标的波动揭示GDM的发生为近年来研究热点之一。现有研究发现孕早期血常规指标的变化与GDM发生发展存在关联[10-12]。孕早期空腹血糖水平升高时,孕中期罹患GDM的风险明显增加[13]。但既往研究多为回顾性研究且只涉及单个指标的分析,具有一定的局限性。因此,本研究基于前瞻性出生人口队列探究孕早期一般状况联合血常规指标、FPG等检化验指标对孕中期GDM发病的关联及预测作用,以期为GDM的早期识别和科学防控提供新的依据。
本研究为“建立出生人口队列开展重大出生缺陷风险研究”的国家重点计划专项(2016YFC1000100)的子研究。依据纳排标准,纳入2018年10月—2022年12月在甘肃省妇幼保健院(甘肃省中心医院)进行系统产检并确定在本院分娩的1 422名孕早期孕妇为研究对象。所有研究对象在调查前均被告知研究内容并签署知情同意书,以确保所有研究者均自愿参与。且该研究已获甘肃省妇幼保健院(甘肃省中心医院)伦理委员会批准,批准号:[2017]院伦审研第(13)号。
本研究纳入标准为:(1)年龄≥18岁;(2)孕周为7~12+周;(3)在本院建档立卡,并有独立的ID号可供数据查询;(4)孕妇在规定时间内完成检查和问卷调查;(5)无精神类疾病,具有正常沟通表达理解能力,可以清楚且准确地回复调查者的提问。排除标准:(1)因流产、死胎而终止妊娠;(2)大量信息缺失或关键信息不全;(3)未怀孕时已患有慢性疾病,如:糖尿病、乙型肝炎、生殖道炎症、牙周炎等;(4)缺少GDM诊断证明;(5)失访。
由经受过专业培训的调查人员采用《建立出生人口队列开展重大出生缺陷风险研究》执行手册进行随访调查,调查内容包括基线资料、实验室检查指标、GDM确诊病历等信息。
(1)基线资料:包括孕妇的年龄、民族、孕前身体质量指数(BMI)、文化程度、主动/被动吸烟、饮酒、孕次、产次等。其中,BMI=体重(kg)/身高的二次方(m2),研究者依据《中国成人超重和肥胖预防控制指南》对BMI水平分为消瘦、正常、超重和肥胖三类[14]。年龄以35岁为高龄妊娠分界线。主动/被动吸烟定义为孕妇/配偶每天吸烟至少一支,连续半年以上。饮酒定义为每月饮酒频率≥1次,连续3个月以上(不限制酒的种类)。
(2)实验室检测指标:包括孕7~12+周的红细胞计数(RBC)、白细胞计数(WBC)、中性粒细胞计数(NEUT)、淋巴细胞计数(LYMPH)、血红蛋白(HGB)、血小板计数(PLT)、红细胞压积(PCV)、FPG。所有实验室检测指标为单次测量,如有重复检测数据,一般选择在纳入时间范围内最后一次检测数据为准。检测仪器为希森美康XN-550全自动五分类血液分析仪和罗氏7600全自动生化分析仪及其配套试剂。
(3)GDM的诊断标准:则根据中华医学会所提出的《妊娠期高血糖诊治指南(2022)》于妊娠24—28周进行口服葡萄糖耐量试验(oral glucose tolerance test,OGTT),OGTT检查前8~10 h开始禁食,检查时,5 min内口服75 g葡萄糖,空腹、口服葡萄糖后1h和2h的血糖阈值分别为5.1、10.0和8.5 mmol/L,如任何一个时间点血糖值达到或超过上述标准即诊断为GDM[15]
采用Epidata 3.1完成数据双录入与校准,使用Excel 2016建立数据库,采用SPSS 26.0和R 4.4.0完成统计分析。服从正态分布的连续性变量以(均值±标准差)表示,不服从正态分布的连续性变量则以[MP25P75)]表示,分类变量选用n(%)来表示。采用两独立样本t检验/Mann-Whitney U检验比较连续性变量在GDM组和非GDM组之间的差异;采用χ2检验与Fisher精确概率法比较分类变量组间差异。WBC、NEUT、LYMPH、HGB和FPG根据四分位数进行分组。以最低浓度组(Q1)为参考类别,分别进行单因素和多因素logistic回归分析,计算比值比(odds ratio,OR)和95%置信区间(confidence interval,CI)。使用方差膨胀系数(variance inflation factor, VIF)检验变量间是否存在共线性(VIF<10被认为不存在共线性)。进一步的采用限制性立方样条(restricted cubic spline,RCS)模型明确WBC和FPG与GDM之间的剂量-反应关系。此外,根据分娩年龄、孕前BMI、孕次、产次开展亚组分析。最后,采用受试者工作特征曲线(receiver operating characteristic curve,ROC)评价孕早期血检验指标预测GDM发生的能力,并计算曲线下面积(area under curve,AUC)。统计学检验均为双侧检验,检验水准α=0.05。
本研究最终共纳入1 422名孕早期孕妇,随访妊娠结局,其中154人确诊为GDM。经分析,GDM组中年龄≥35岁、超重和肥胖孕妇所占比例均较高,GDM组孕妇孕早期的WBC、NEUT、LYMPH、HGB、FPG水平测值均高,和队列内健康孕妇比,差异有统计学意义(P<0.05)。见表1
通过单因素logistic回归(模型1)发现,孕早期最高第四分位数(Q4)组WBC、LYMPH、HGB、FPG与GDM发生的风险之间呈正相关,OR值分别为1.878(95%CI: 1.163~3.033)、1.758(95%CI:1.074~2.878)、1.654(95%CI:1.043~2.623)、6.363(95%CI:3.588~11.285)。模型2在调整孕妇年龄、孕前BMI、孕次、产次、受孕方式后,发现关联仍然成立。见表2
共线性诊断结果显示,WBC、NEUT、LYMPH、HGB、FPG的VIF分别为17.553、13.474、2.673、1.060、1.007,因WBC和NEUT的VIF均大于10,提示存在共线性问题,因此不能同时纳入模型中。多因素logistic回归结果显示,仅孕早期WBC和FPG与GDM发生仍具有关联(P<0.05)。见表3
通过RCS模型分析发现,在调整孕前BMI、年龄、孕次、产次、受孕方式后,孕早期WBC、FPG与GDM的发病风险均存在正向线性剂量-反应关系(P foroverall<0.05;P fornonlinear>0.05)。见图1
为探究研究结果的稳定性,根据分娩年龄、孕前BMI、孕次、产次分为不同亚组开展分析,结果显示基本保持一致。在各个细分的亚组中,孕早期FPG上升均会导致孕妇罹患GDM风险上升(P<0.05);而在BMI分组为消瘦和产次≥2次的孕妇中,未观察到WBC上升会致使孕妇罹患GDM的风险上升(P>0.05)。见表4
以孕早期FPG、WBC和HGB作为检验变量绘制ROC曲线,结果显示:相较于FPG(AUC=0.692,95%CI: 0.647~0.736)、WBC(AUC=0.576,95%CI:0.527~0.625)和HGB(AUC=0.566,95%CI=0.517~0.615)单独预测,孕早期WBC联合FPG、HGB、年龄、孕前BMI预测GDM(AUC=0.736,95%CI:0.695~0.776)效能更好。见图2
近年来,GDM在孕妇中的发病率逐年增高,发现即已确诊,专家一直在探寻疾病早期诊断的预警方式以防患于未然。本研究发现:孕早期WBC、FPG为孕中期GDM的危险因素且存在线性的剂量-反应关系;高龄、超重和肥胖的孕妇,孕早期WBC、FPG水平增高,其孕中期GDM发病风险显著增加。年龄、孕前BMI联合孕早期WBC、FPG对GDM发生发展具有较好的预测能力。
GDM表现出低度慢性炎症表型,可引起孕早期血常规指标波动[16]。而血常规指标改变能否在孕早期即被观测,是本研究需要探讨的问题之一。研究发现,孕早期血常规指标中的WBC、LYMPH、HGB及FPG水平上升均会增加孕中期GDM的患病风险,孕早期WBC水平Q4组后期发生GDM的风险为Q1组的1.774倍(95%CI:1.088~2.893),与既往学者研究结论相同[17-18]。研究还发现孕早期LYMPH Q4组发生GDM的风险为Q1组的1.712倍(95%CI:1.035~2.833),但未观察到NEUT为GDM的危险因素。而Ye[19]和Yang等[20]研究团队均报道孕早期NEUT、LYMPH的增加均与较高的GDM风险有关,Liu等[21]观察到GDM组NEUT高于非GDM组,但经logistic回归分析却未发现显著性差异(P>0.05)。曹等[22]学者发现孕早期LYMPH上升未增加GDM患病风险。总之,目前有关孕早期NEUT、LYMPH与GDM的研究结论尚存争议,有待进一步探讨。
HGB同样为GDM的预测指标之一,本研究发现HGB Q4组发生GDM的风险为1.597(95%CI:1.004~2.555),该结论与闫明鑫、谭培松等[23-24]学者报道一致,但与Zein等[25]将孕早期HGB水平按照125 g/L阈值分类后报道结果向相悖。考虑与指标分类依据不同有关。孕早期HGB上升与GDM发病风险之间的机制关联尚不明确。部分学者曾提出假设:血清中铁离子具有强氧化性,能够破坏胰岛素传导,进而表现胰岛素抵抗。同时过量的铁增加胰岛β细胞的氧化应激,影响胰岛素合成与分泌,进而导致GDM[26]
本研究还发现,孕早期FPG升高与GDM发病风险存在正向的线性剂量-反应关系(P fornon-linear= 0.076>0.05)。而一项华南地区的研究曾报道:孕早期FPG升高与GDM发病风险有关,但关联为非线性的剂量-反应关系(P fornon-linear<0.01)[27]。考虑线性与非线性差异主要为纳入协变量种类不同以及RCS曲线节点设置不同。
将一般状况指标年龄、孕前BMI和孕早期检化验指标WBC、FPG联合,开展GDM发病预测研究。结果发现,孕早期WBC的AUC为0.576(95%CI:0.527~0.625),与曹等[22]报道基本一致(AUC=0.590,95%CI:0.548~0.631)。FPG的AUC为0.692(95%CI:0.647~0.736),高于Tong等[27]报到的结果(AUC= 0.608,95%CI:0.598~0.617),低于对土耳其孕妇调查所得出的结果(FPG:AUC=0.831,95%CI:0.777~0.884)[28]。孕早期WBC、FPG会同年龄、孕期BMI联合预测效果(AUC=0.733,95%CI:0.692~0.775)最优,略优于曹等[22]提出年龄、孕前BMI、孕妇职业、孕早期血糖、孕早期增重速率、孕早期WBC的结果(AUC= 0.706,95%CI:0.669~0.743),AUC差异可能与纳入变量的血糖值是否为空腹有关。
受单中心研究、研究样本量有限以及孕早期7~12+周检化验指标的波动性影响,本研究也存在一定的局限性。未来研究有待通过扩大中心及样本量、增加检测指标及检测频率等方式进一步深入。总之,通过本项队列研究,我们呼吁医护工作者着重关注孕妇年龄、基础BMI以及孕早期WBC、FPG水平,其对联合预测GDM的发生发展风险具有一定的价值及指导意义。
  • 国家重点计划专项(2016YFC1000100)
  • 甘肃省自然科学基金(22JR11RA177)
  • 甘肃省联合科研基金一般项目(24JRRA938)
  • 国家儿童健康与疾病临床医学研究中心临床医学研究一般项目(NCRCCHD-2022-GP-17)
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doi: 10.20043/j.cnki.MPM.202411233
  • 接收时间:2024-11-13
  • 首发时间:2026-03-18
  • 出版时间:2025-06-10
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  • 收稿日期:2024-11-13
基金
国家重点计划专项(2016YFC1000100)
甘肃省自然科学基金(22JR11RA177)
甘肃省联合科研基金一般项目(24JRRA938)
国家儿童健康与疾病临床医学研究中心临床医学研究一般项目(NCRCCHD-2022-GP-17)
作者信息
    1.甘肃中医药大学公共卫生学院,甘肃 兰州 730101
    2.甘肃省妇幼保健院科研中心,甘肃 兰州 730050

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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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