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Article(id=1311349857432994402, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1311349802038821144, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202512073, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1765036800000, receivedDateStr=2025-12-07, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790579769714, onlineDateStr=2026-09-28, pubDate=1783612800000, pubDateStr=2026-07-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790579769714, onlineIssueDateStr=2026-09-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790579769714, creator=13701087609, updateTime=1790579769714, updator=13701087609, issue=Issue{id=1311349802038821144, tenantId=1146029695717560320, journalId=1227665162245664772, year='2026', volume='53', issue='13', pageStart='2305', pageEnd='2496', issueExtLink='null', onlineDate='null', pubDate='1783612800000', pubDateStr='2026-07-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790579756507, creator='13701087609', updateTime=1790588376143, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1311385955492258162, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1311349802038821144, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1311385955496452467, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1311349802038821144, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=2398, endPage=2402, ext={EN=ArticleExt(id=1311349857634320995, articleId=1311349857432994402, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Association between distinct gestational weight gain patterns and the risk of gestational diabetes mellitus, columnId=1228016568949474136, journalTitle=Modern Preventive Medicine, columnName=Child and Adolescent Health, Maternal and Child Health, runingTitle=null, highlight=null, articleAbstract=
Objective

To examine the association between different gestational weight gain (GWG) patterns and the risk of gestational diabetes mellitus (GDM), and to identify high-risk weight gain characteristics that may inform GWG management and early prevention of GDM.

Methods

A prospective cohort study was conducted among 312 pregnant women who completed follow-up at the Obstetrics Department of the First Affiliated Hospital of Shanxi Medical College between January 2022 and December 2024. According to the Institute of Medicine (IOM) recommendations, and based on pre-pregnancy body mass index(BMI) and weight changes across the first, second, and third trimesters, latent class growth analysis (LCGA) was used to classify GWG trajectories into three patterns: steady-appropriate, moderately rapid, and persistently rapid. GDM was diagnosed using the results of a 24-28-week oral glucose tolerance test (OGTT). Multivariable logistic regression models were applied to assess the independent associations between GWG patterns and GDM, and model performance was evaluated using the Hosmer-Lemeshow goodness-of-fit test and receiver operating characteristic (ROC) curve analysis.

Results

The overall incidence of GDM was 22.44% (70/312). Among all participants, 55.13% (172/312) showed a steady-appropriate GWG pattern, 31.09% (97/312) showed a moderately rapid pattern, and 13.78% (43/312) showed a persistently rapid pattern, with corresponding GDM incidences of 14.53% (25/172), 29.90% (29/97), and 37.21% (16/43), respectively (P<0.001). Multivariable analysis showed that compared with the steady-appropriate pattern, the moderately rapid pattern was associated with an increased risk of GDM(OR=1.68, 95% CI: 1.02-2.77, P=0.041), and the persistently rapid pattern showed a stronger association (OR=2.95, 95% CI:1.54-5.64, P=0.001). Pre-pregnancy BMI (OR=1.10, 95% CI: 1.01-1.21), family history of diabetes (OR=2.13, 95% CI: 1.13-4.00), and low physical activity level (OR=1.89, 95% CI: 1.04-3.44) were also independently associated with GDM (all P<0.05). The Hosmer-Lemeshow test indicated good model fit (P=0.537). The area under the ROC curve was 0.781 (95% CI: 0.726-0.835), with a sensitivity of 72.86% and a specificity of 73.44%.

Conclusion

GWG patterns are closely associated with GDM. Monitoring abnormal weight gain trends during pregnancy may have indicative value for metabolic health management.

, authors=Li-fang WEN1, Jie DING2, Qian WU3, authorsList=Li-fang WEN, Jie DING, Qian WU, authorCompany=null, correspAuthors=Li-fang WEN, 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=1311349860616471152, articleId=1311349857432994402, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=不同孕期体重增长模式与妊娠糖尿病发生风险的关联分析, columnId=1228016570031604578, journalTitle=现代预防医学, columnName=儿少卫生与妇幼保健, runingTitle=null, highlight=null, articleAbstract=
目的

探讨不同孕期体重增长模式与妊娠糖尿病（gestational diabetes mellitus，GDM）发生风险的关系，识别高风险体重增长特征，为孕期体重管理和GDM早期预防提供依据。

方法

采用前瞻性队列研究，于2022年1月—2024年12月在山西医药学院附属第一医院产科纳入完成随访的孕妇312例。依据国际医学研究所推荐标准，结合孕前身体质量指数（body mass index，BMI）及孕早、中、晚期体重变化，采用潜在类别增长模型将体重增长轨迹分为平稳适宜型、中度快速型和持续过快型。GDM诊断依据孕24～28周口服葡萄糖耐量试验结果。通过多因素logistic回归分析不同体重增长模式与GDM的独立关系，并以Hosmer-Lemeshow检验及受试者工作特征（receiver operating characteristic，ROC）曲线评价模型性能。

结果

GDM总体发生率为22.44%（70/312）。平稳适宜型、中度快速型和持续过快型分别占55.13%（172/312）、31.09%（97/312）和13.78%（43/312），GDM发生率依次为14.53%（25/172）、29.90%（29/97）和37.21%（16/43）（P<0.001）。多因素分析显示，与平稳适宜型相比，中度快速型GDM风险比（OR）为1.68（95%CI：1.02～2.77，P=0.041），持续过快型OR为2.95（95%CI：1.54～5.64，P=0.001）。孕前BMI（OR=1.10，95%CI：1.01～1.21）、家族糖尿病史（OR=2.13，95%CI：1.13～4.00）及低体力活动水平（OR=1.89，95%CI：1.04～3.44）亦为独立影响因素（均P<0.05）。Hosmer-Lemeshow检验P=0.537，ROC曲线下面积为0.781（95%CI：0.726～0.835），敏感度72.86%，特异度73.44%。

结论

孕期体重增长轨迹表现出的不同模式与GDM发生密切相关。关注孕期体重的异常增长态势，对加强孕期代谢健康管理具有一定的提示意义。

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文丽芳（1982—），女，硕士，副教授，研究方向：妇科肿瘤的基础与临床研究

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文丽芳，E-mail：
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文丽芳（1982—），女，硕士，副教授，研究方向：妇科肿瘤的基础与临床研究

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BMJ Open, 2024, 14(11): e091258., articleTitle=Association between gestational diabetes mellitus diagnostic criteria and adverse pregnancy outcomes-a systematic review and meta-analysis of adjusted effect sizes from studies using current diagnostic criteria, refAbstract=null), Reference(id=1311349866484302506, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349857432994402, doi=null, pmid=null, pmcid=null, year=2017, volume=28, issue=3, pageStart=419, pageEnd=427, url=null, language=null, rfNumber=[13], rfOrder=12, authorNames=Macdonald SC, Bodnar LM, Himes KP, journalName=Epidemiology, refType=null, unstructuredReference=Macdonald SC, Bodnar LM, Himes KP, et al. Patterns of gestational weight gain in early pregnancy and risk of gestational diabetes mellitus[J]. Epidemiology, 2017, 28(3): 419-427., articleTitle=Patterns of gestational weight gain in early pregnancy and risk of gestational diabetes mellitus, refAbstract=null), Reference(id=1311349866576577195, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349857432994402, doi=null, pmid=null, pmcid=null, year=2024, volume=20, issue=3, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[14], rfOrder=13, authorNames=Lin L, Huang Y, Chen L, journalName=Maternal & Child Nutrition, refType=null, unstructuredReference=Lin L, Huang Y, Chen L, et al. Gestational weight trajectory and risk of adverse pregnancy outcomes among women with gestational diabetes mellitus: a retrospective cohort study[J]. Maternal & Child Nutrition, 2024, 20(3): e13645., articleTitle=Gestational weight trajectory and risk of adverse pregnancy outcomes among women with gestational diabetes mellitus: a retrospective cohort study, refAbstract=null), Reference(id=1311349866673046188, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349857432994402, doi=null, pmid=null, pmcid=null, year=2024, volume=24, issue=1, pageStart=594, pageEnd=null, url=null, language=null, rfNumber=[15], rfOrder=14, authorNames=Xie WT, Zhang LW, Cheng JY, journalName=BMC Public Health, refType=null, unstructuredReference=Xie WT, Zhang LW, Cheng JY, et al. Physical activity during pregnancy and the risk of gestational diabetes mellitus: a systematic review and dose-response meta-analysis[J]. 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LIFE-BASEL, 2024, 14(6): 755., articleTitle=Physical activity during pregnancy and risk of gestational diabetes mellitus:a Meta-Review, refAbstract=null), Reference(id=1311349866891149999, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349857432994402, doi=null, pmid=null, pmcid=null, year=2023, volume=25, issue=1, pageStart=175, pageEnd=null, url=null, language=null, rfNumber=[18], rfOrder=17, authorNames=Kamiński M, Mierzyński R, Poniedziałek-Czajkowska E, journalName=International Journal of Molecular Sciences, refType=null, unstructuredReference=Kamiński M, Mierzyński R, Poniedziałek-Czajkowska E, et al. Comparative evaluation of adipokine metrics for the diagnosis of gestational diabetes mellitus[J]. 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Gynecological Endocrinology, 2023, 39(1): 2183049., articleTitle=Maternal circulating leptin, tumor necrosis factor-alpha, and interleukin-6 in association with gestational diabetes mellitus: a systematic review and meta-analysis, refAbstract=null), Reference(id=1311349867054727857, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349857432994402, doi=null, pmid=null, pmcid=null, year=2025, volume=35, issue=10, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[20], rfOrder=19, authorNames=Lyu J, Peng Y, Yang L, journalName=Nutrition, Metabolism, and Cardiovascular Diseases, refType=null, unstructuredReference=Lyu J, Peng Y, Yang L, et al. Development and validation of a prediction model for gestational diabetes mellitus based on clinical characteristics and laboratory biomarkers among Chinese women[J]. Nutrition, Metabolism, and Cardiovascular Diseases, 2025, 35(10): 104065., articleTitle=Development and validation of a prediction model for gestational diabetes mellitus based on clinical characteristics and laboratory biomarkers among Chinese women, refAbstract=null), Reference(id=1311349867130225330, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349857432994402, doi=null, pmid=null, pmcid=null, year=2023, volume=23, issue=1, pageStart=469, pageEnd=null, url=null, language=null, rfNumber=[21], rfOrder=20, authorNames=Cubillos G, Monckeberg M, Plaza A, journalName=BMC Pregnancy and Childbirth, refType=null, unstructuredReference=Cubillos G, Monckeberg M, Plaza A, et al. Development of machine learning models to predict gestational diabetes risk in the first half of pregnancy[J]. BMC Pregnancy and Childbirth, 2023, 23(1): 469., articleTitle=Development of machine learning models to predict gestational diabetes risk in the first half of pregnancy, refAbstract=null)], funds=[Fund(id=1311349865427337885, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349857432994402, awardId=2022L672, language=CN, fundingSource=山西省高校科技创新计划项目(2022L672), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1311349860868129393, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349857432994402, xref=1., ext=[AuthorCompanyExt(id=1311349860876518002, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349857432994402, companyId=1311349860868129393, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Shanxi Medical College, Fenyang, Shanxi 032200, China), AuthorCompanyExt(id=1311349860889100915, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349857432994402, companyId=1311349860868129393, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.山西医药学院，山西 汾阳 032200)]), AuthorCompany(id=1311349860960404084, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349857432994402, xref=2., ext=[AuthorCompanyExt(id=1311349860964598389, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349857432994402, companyId=1311349860960404084, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.山西医药学院附属第一医院 介入科)]), AuthorCompany(id=1311349861056873078, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349857432994402, xref=3., ext=[AuthorCompanyExt(id=1311349861069455991, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349857432994402, companyId=1311349861056873078, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3.山西医药学院附属第一医院妇产科)])], figs=[ArticleFig(id=1311349862843646609, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349857432994402, language=EN, label=Table 1, caption=

Comparison of general characteristics of pregnant women between GDM group and non-GDM group ［（）， n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
指标GDM组（n=70）非GDM组（n=242）t/χ2值P值
年龄（岁）29.9±3.829.8±4.00.160.873
孕前BMI（kg/m2）24.1±3.222.3±3.53.170.002
家族糖尿病史19（27.14）29（11.89）8.370.004
受教育程度（大专及以上）45（64.29）160（66.12）0.070.784
吸烟史5（7.14）8（3.31）—0.285a
孕次≥2次22（31.43）57（23.55）1.650.199
孕早期体重增长（kg）2.8±1.21.9±1.05.56<0.001
孕中期体重增长（kg）6.5±2.15.9±2.02.060.040
孕晚期体重增长（kg）4.3±1.73.8±1.52.100.036
体力活动中高水平34（48.57）167（69.07）7.570.006
), ArticleFig(id=1311349862914949778, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349857432994402, language=CN, label=表1, caption=

GDM组与非GDM组孕妇一般特征比较［（），n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
指标GDM组（n=70）非GDM组（n=242）t/χ2值P值
年龄（岁）29.9±3.829.8±4.00.160.873
孕前BMI（kg/m2）24.1±3.222.3±3.53.170.002
家族糖尿病史19（27.14）29（11.89）8.370.004
受教育程度（大专及以上）45（64.29）160（66.12）0.070.784
吸烟史5（7.14）8（3.31）—0.285a
孕次≥2次22（31.43）57（23.55）1.650.199
孕早期体重增长（kg）2.8±1.21.9±1.05.56<0.001
孕中期体重增长（kg）6.5±2.15.9±2.02.060.040
孕晚期体重增长（kg）4.3±1.73.8±1.52.100.036
体力活动中高水平34（48.57）167（69.07）7.570.006
), ArticleFig(id=1311349862977864339, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349857432994402, language=EN, label=Table 2, caption=

Classification and trajectory characteristics of gestational weight gain patterns ［n（%）， （）］

, figureFileSmall=null, figureFileBig=null, tableContent=
模式类型例数（%）孕早期体重增长（kg）孕中期体重增长（kg）孕晚期体重增长（kg）总体增重（kg）BICAICEntropy
平稳适宜型172（55.13）1.8±0.95.6±1.93.9±1.411.3±2.11 867.41 821.60.812
中度快速型97（31.09）2.4±1.06.4±1.84.4±1.613.2±2.6
持续过快型43（13.78）3.1±1.37.5±2.24.9±1.814.1±2.3
), ArticleFig(id=1311349863057556116, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349857432994402, language=CN, label=表2, caption=

孕期体重增长模式分类及轨迹特征［n（%），（）］

, figureFileSmall=null, figureFileBig=null, tableContent=
模式类型例数（%）孕早期体重增长（kg）孕中期体重增长（kg）孕晚期体重增长（kg）总体增重（kg）BICAICEntropy
平稳适宜型172（55.13）1.8±0.95.6±1.93.9±1.411.3±2.11 867.41 821.60.812
中度快速型97（31.09）2.4±1.06.4±1.84.4±1.613.2±2.6
持续过快型43（13.78）3.1±1.37.5±2.24.9±1.814.1±2.3
), ArticleFig(id=1311349863124664981, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349857432994402, language=EN, label=Table 3, caption=

Comparison of different weight gain patterns with the incidence of GDM

, figureFileSmall=null, figureFileBig=null, tableContent=
模式类型总例数GDM例数（%）χ2值P值
平稳适宜型17225（14.53）
中度快速型9729（29.90）
持续过快型4316（37.21）14.67<0.001
), ArticleFig(id=1311349863204356758, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349857432994402, language=CN, label=表3, caption=

不同体重增长模式与GDM发生率比较

, figureFileSmall=null, figureFileBig=null, tableContent=
模式类型总例数GDM例数（%）χ2值P值
平稳适宜型17225（14.53）
中度快速型9729（29.90）
持续过快型4316（37.21）14.67<0.001
), ArticleFig(id=1311349863271465623, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349857432994402, language=EN, label=Table 4, caption=

Univariable logistic regression analysis of factors associated with the risk of GDM

, figureFileSmall=null, figureFileBig=null, tableContent=
变量β Wald χ2值OR值（95%CI）P值
年龄0.0210.0340.381.02（0.95～1.09）0.538
孕前BMI0.1160.0418.011.12（1.04～1.22）0.005
家族糖尿病史0.8620.3187.332.37（1.29～4.37）0.007
吸烟史0.6190.4821.651.86（0.73～4.76）0.198
孕早期体重增长0.4720.13711.901.60（1.23～2.10）0.001
孕期体力活动（低 vs. 中高）0.7440.2926.492.10（1.19～3.68）0.011
体重增长模式（持续过快 vs. 平稳）1.1120.33810.833.04（1.57～5.86）0.001
), ArticleFig(id=1311349864940798616, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349857432994402, language=CN, label=表4, caption=

GDM发生的单因素logistic回归分析

, figureFileSmall=null, figureFileBig=null, tableContent=
变量β Wald χ2值OR值（95%CI）P值
年龄0.0210.0340.381.02（0.95～1.09）0.538
孕前BMI0.1160.0418.011.12（1.04～1.22）0.005
家族糖尿病史0.8620.3187.332.37（1.29～4.37）0.007
吸烟史0.6190.4821.651.86（0.73～4.76）0.198
孕早期体重增长0.4720.13711.901.60（1.23～2.10）0.001
孕期体力活动（低 vs. 中高）0.7440.2926.492.10（1.19～3.68）0.011
体重增长模式（持续过快 vs. 平稳）1.1120.33810.833.04（1.57～5.86）0.001
), ArticleFig(id=1311349865037267609, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349857432994402, language=EN, label=Table 5, caption=

Multivariable logistic regression analysis of factors associated with the risk of GDM

, figureFileSmall=null, figureFileBig=null, tableContent=
变量β Wald χ2值OR值（95%CI）P值
孕前BMI0.0970.0444.851.10（1.01～1.21）0.028
家族糖尿病史0.7560.3225.522.13（1.13～4.00）0.019
孕期体力活动（低 vs .中高）0.6340.3064.291.89（1.04～3.44）0.038
中度快速型（vs .平稳型）0.5200.2544.201.68（1.02～2.77）0.041
持续过快型（vs. 平稳型）1.0820.32511.092.95（1.54～5.64）0.001
常数-3.4130.78219.03—<0.001
), ArticleFig(id=1311349865133736602, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349857432994402, language=CN, label=表5, caption=

GDM发生的多因素logistic回归分析

, figureFileSmall=null, figureFileBig=null, tableContent=
变量β Wald χ2值OR值（95%CI）P值
孕前BMI0.0970.0444.851.10（1.01～1.21）0.028
家族糖尿病史0.7560.3225.522.13（1.13～4.00）0.019
孕期体力活动（低 vs .中高）0.6340.3064.291.89（1.04～3.44）0.038
中度快速型（vs .平稳型）0.5200.2544.201.68（1.02～2.77）0.041
持续过快型（vs. 平稳型）1.0820.32511.092.95（1.54～5.64）0.001
常数-3.4130.78219.03—<0.001
), ArticleFig(id=1311349865217622683, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349857432994402, language=EN, label=Table 6, caption=

Model fitting and prediction performance indicators

, figureFileSmall=null, figureFileBig=null, tableContent=
指标值
Hosmer-Lemeshow χ25.32
P值0.537
AUC（95%CI）0.781（0.726～0.835）
最佳界值0.22
敏感度（%）72.86
特异度（%）73.44
Youden指数0.463
), ArticleFig(id=1311349865309897372, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349857432994402, language=CN, label=表6, caption=

模型拟合与预测性能指标

, figureFileSmall=null, figureFileBig=null, tableContent=
指标值
Hosmer-Lemeshow χ25.32
P值0.537
AUC（95%CI）0.781（0.726～0.835）
最佳界值0.22
敏感度（%）72.86
特异度（%）73.44
Youden指数0.463
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不同孕期体重增长模式与妊娠糖尿病发生风险的关联分析
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文丽芳 1 , 丁洁 2 , 武茜 3
现代预防医学 | 儿少卫生与妇幼保健 2026,53(13): 2398-2402
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现代预防医学 |儿少卫生与妇幼保健 2026 , 53 (13) : 2398 -2402
不同孕期体重增长模式与妊娠糖尿病发生风险的关联分析
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文丽芳1 , 丁洁2, 武茜3
作者信息
  • 1.山西医药学院，山西 汾阳 032200
  • 2.山西医药学院附属第一医院 介入科
  • 3.山西医药学院附属第一医院妇产科
通讯作者:
文丽芳，E-mail：
作者简介:

文丽芳（1982—），女，硕士，副教授，研究方向：妇科肿瘤的基础与临床研究

Association between distinct gestational weight gain patterns and the risk of gestational diabetes mellitus
Li-fang WEN1 , Jie DING2, Qian WU3
Affiliations
  • Shanxi Medical College, Fenyang, Shanxi 032200, China
出版时间: 2026-07-10 doi: 10.20043/j.cnki.MPM.202512073
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摘要
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目的

探讨不同孕期体重增长模式与妊娠糖尿病（gestational diabetes mellitus，GDM）发生风险的关系，识别高风险体重增长特征，为孕期体重管理和GDM早期预防提供依据。

方法

采用前瞻性队列研究，于2022年1月—2024年12月在山西医药学院附属第一医院产科纳入完成随访的孕妇312例。依据国际医学研究所推荐标准，结合孕前身体质量指数（body mass index，BMI）及孕早、中、晚期体重变化，采用潜在类别增长模型将体重增长轨迹分为平稳适宜型、中度快速型和持续过快型。GDM诊断依据孕24～28周口服葡萄糖耐量试验结果。通过多因素logistic回归分析不同体重增长模式与GDM的独立关系，并以Hosmer-Lemeshow检验及受试者工作特征（receiver operating characteristic，ROC）曲线评价模型性能。

结果

GDM总体发生率为22.44%（70/312）。平稳适宜型、中度快速型和持续过快型分别占55.13%（172/312）、31.09%（97/312）和13.78%（43/312），GDM发生率依次为14.53%（25/172）、29.90%（29/97）和37.21%（16/43）（P<0.001）。多因素分析显示，与平稳适宜型相比，中度快速型GDM风险比（OR）为1.68（95%CI：1.02～2.77，P=0.041），持续过快型OR为2.95（95%CI：1.54～5.64，P=0.001）。孕前BMI（OR=1.10，95%CI：1.01～1.21）、家族糖尿病史（OR=2.13，95%CI：1.13～4.00）及低体力活动水平（OR=1.89，95%CI：1.04～3.44）亦为独立影响因素（均P<0.05）。Hosmer-Lemeshow检验P=0.537，ROC曲线下面积为0.781（95%CI：0.726～0.835），敏感度72.86%，特异度73.44%。

结论

孕期体重增长轨迹表现出的不同模式与GDM发生密切相关。关注孕期体重的异常增长态势，对加强孕期代谢健康管理具有一定的提示意义。

妊娠糖尿病  /  孕期体重增长  /  体重增长模式  /  危险因素  /  Logistic回归
Objective

To examine the association between different gestational weight gain (GWG) patterns and the risk of gestational diabetes mellitus (GDM), and to identify high-risk weight gain characteristics that may inform GWG management and early prevention of GDM.

Methods

A prospective cohort study was conducted among 312 pregnant women who completed follow-up at the Obstetrics Department of the First Affiliated Hospital of Shanxi Medical College between January 2022 and December 2024. According to the Institute of Medicine (IOM) recommendations, and based on pre-pregnancy body mass index(BMI) and weight changes across the first, second, and third trimesters, latent class growth analysis (LCGA) was used to classify GWG trajectories into three patterns: steady-appropriate, moderately rapid, and persistently rapid. GDM was diagnosed using the results of a 24-28-week oral glucose tolerance test (OGTT). Multivariable logistic regression models were applied to assess the independent associations between GWG patterns and GDM, and model performance was evaluated using the Hosmer-Lemeshow goodness-of-fit test and receiver operating characteristic (ROC) curve analysis.

Results

The overall incidence of GDM was 22.44% (70/312). Among all participants, 55.13% (172/312) showed a steady-appropriate GWG pattern, 31.09% (97/312) showed a moderately rapid pattern, and 13.78% (43/312) showed a persistently rapid pattern, with corresponding GDM incidences of 14.53% (25/172), 29.90% (29/97), and 37.21% (16/43), respectively (P<0.001). Multivariable analysis showed that compared with the steady-appropriate pattern, the moderately rapid pattern was associated with an increased risk of GDM(OR=1.68, 95% CI: 1.02-2.77, P=0.041), and the persistently rapid pattern showed a stronger association (OR=2.95, 95% CI:1.54-5.64, P=0.001). Pre-pregnancy BMI (OR=1.10, 95% CI: 1.01-1.21), family history of diabetes (OR=2.13, 95% CI: 1.13-4.00), and low physical activity level (OR=1.89, 95% CI: 1.04-3.44) were also independently associated with GDM (all P<0.05). The Hosmer-Lemeshow test indicated good model fit (P=0.537). The area under the ROC curve was 0.781 (95% CI: 0.726-0.835), with a sensitivity of 72.86% and a specificity of 73.44%.

Conclusion

GWG patterns are closely associated with GDM. Monitoring abnormal weight gain trends during pregnancy may have indicative value for metabolic health management.

Gestational diabetes mellitus  /  Gestational weight gain  /  Weight gain patterns  /  Risk factors  /  Logistic regression
文丽芳, 丁洁, 武茜. 不同孕期体重增长模式与妊娠糖尿病发生风险的关联分析. 现代预防医学, 2026 , 53 (13) : 2398 -2402 . DOI: 10.20043/j.cnki.MPM.202512073
Li-fang WEN, Jie DING, Qian WU. Association between distinct gestational weight gain patterns and the risk of gestational diabetes mellitus[J]. Modern Preventive Medicine, 2026 , 53 (13) : 2398 -2402 . DOI: 10.20043/j.cnki.MPM.202512073
正文
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妊娠糖尿病（gestational diabetes mellitus，GDM）是孕期常见的糖代谢异常，其特征为妊娠期间首次出现或发现的糖耐量减退[1]。近年来，随着肥胖率上升、孕妇年龄增长及生活方式转变，GDM的发病率呈持续上升趋势[2]。大量证据表明，GDM不仅增加巨大儿、妊娠期高血压、早产及剖宫产等不良妊娠结局风险，还对母体远期代谢健康造成持续影响[3]。研究显示，GDM妇女产后发生2型糖尿病和心血管疾病的风险显著升高，其子代亦更易发生肥胖及代谢紊乱[4]。因此，针对可干预危险因素的早期识别与管理，是孕期健康促进和疾病一级预防的关键环节。
孕期体重增长是反映孕妇代谢适应与胎儿营养供给的重要指标。既往研究多聚焦于孕期总增重与GDM风险的总体关联[5]，但不同孕期阶段体重增长速率差异的作用尚未得到充分阐明。体重增长过快可能通过加重胰岛素抵抗、改变脂肪因子水平而增加糖代谢负担，体重增长不足则可能影响胎盘功能及胎儿发育[6]。尽管国际医学研究所（Institute of Medicine，IOM）提出了孕期体重增长的推荐范围，但其在不同孕前身体质量指数（body mass index，BMI）人群中的适用性及与代谢风险的对应关系仍不明确[7]。近年来，潜在类别增长模型（latent class growth analysis，LCGA）等统计方法被应用于孕期体重变化的轨迹研究，为揭示孕妇间体重增长模式的异质性提供了新的分析视角[8]。基于此，本研究采用LCGA方法刻画孕期体重增长轨迹，分析不同增长模式与GDM发生风险的关联，以期为孕期体重监测、个体化管理及GDM早期预防提供数据支持和实践参考。
1　资料与方法
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1.1　研究对象
本研究为前瞻性队列研究，于2022年1月—2024年12月在山西医药学院附属第一医院产科连续招募孕妇。本研究样本量依据队列研究两组率的比较进行估算，假设平稳适宜型孕妇GDM发生率为15%，持续过快型孕妇GDM发生率约为35%，设定双侧α=0.05，检验效能1-β=0.80，经PASS软件计算得出至少需样本量约260例；考虑到约15%的失访率，计划至少纳入306例。本研究初始共招募建立孕期档案的孕妇385例。纳入标准为年龄18～45岁，单胎妊娠，孕≤13周在本院建立孕期建档并计划在本院分娩，孕前无糖尿病史且孕期体重监测资料完整。在随访过程中共排除73例，具体包括：多胎妊娠12例、既往内分泌疾病（如甲状腺功能异常）或严重肝肾功能损害10例、孕期体重记录缺失>1次者28例、以及中途终止妊娠或随访资料不全（失访）者23例。所有最终纳入对象均签署知情同意书，实际完成随访并纳入分析者共312例。最终样本量（包含GDM患者70例）满足多因素logistic回归分析中事件数与自变量参数个数比例>10的统计学经验法则，具备充足的统计学检验效能。本研究经山西医药学院附属第一医院医学伦理委员会审查批准（伦理审查批准号：2025024），所有研究对象均签署知情同意书。
1.2　资料收集与变量定义
资料由统一培训的研究人员根据结构化表格前瞻性收集。人口学与一般情况包括年龄、文化程度、孕前BMI、家族糖尿病史、吸烟史及孕次、产次等。孕期体重在特定孕周的例行产检时由专职护士按统一流程测量，使用经定期校准的电子体重秤，记录至整数。具体测量时间点设定为：孕早期（孕12周建档时）、孕中期［孕24～28周进行口服葡萄糖耐量试验（oral glucose tolerance test，OGTT）检查时］及孕晚期（孕32周及36周常规产检时）。整个孕期每位孕妇共提取4次核心体重数据用于后续轨迹分析。孕期体力活动采用国际体力活动问卷（International Physical Activity Questionnaire，IPAQ）评估，按代谢当量（metabolic equivalents，METs）折算后分为低、中、高三个水平。实验室指标主要包括孕24～28周OGTT结果。GDM诊断依据国家相关指南：空腹血糖≥5.1 mmol/L、1 h血糖≥10.0 mmol/L或2 h血糖≥8.5 mmol/L中任一异常即可确诊。
1.3　孕期体重增长模式分类方法
以孕前体重为基线，计算孕早、中、晚期体重增量，并结合IOM推荐标准，按孕前BMI分层界定各孕期合理增长区间。基于孕期体重变化序列，采用LCGA识别体重增长轨迹。模型估计采用最大似然法，逐步增加潜在类别数，并综合比较贝叶斯信息准则（Bayesian information criterion，BIC）、赤池信息准则（Akaike information criterion，AIC）、熵值（entropy）及各类别样本比例，结合临床可解释性确定最优模型。最终将轨迹划分为平稳适宜型、中度快速型和持续过快型三类，根据最大后验概率将个体归入相应类别。
1.4　统计学分析
统计分析采用SPSS 27.0和R 4.2.1软件。计量资料正态分布时以（均数±标准差）表示，组间比较用t检验；非正态分布以中位数（四分位间距）表示，采用Mann–Whitney U检验。计数资料以例数和构成比表示，组间比较采用χ2检验或Fisher确切概率法。首先比较不同体重增长模式及GDM与非GDM组间各变量差异，将P<0.10的变量纳入多因素logistic回归模型。以GDM发生与否为因变量，明确以“不同孕期体重增长模式”为主要暴露因素，将单因素分析中P<0.10的变量（孕前BMI、家族史、体力活动水平等）作为混杂因素进行模型校正，计算比值比（OR）及其95%置信区间。模型拟合优度采用Hosmer-Lemeshow检验评估（P>0.05为拟合良好），预测性能通过受试者工作特征曲线计算曲线下面积、敏感度和特异度。为评估体重增长模式的增量预测价值，本研究首先构建了仅包含传统混杂因素（孕前BMI、家族糖尿病史、体力活动水平）的基础模型，随后在此基础上加入主要暴露因素（体重增长模式）构建扩展模型。通过计算连续型净重新分类指数（net reclassification improvement,NRI）和综合判别改善指数（integrated discrimination improvement,IDI），评价新指标的引入对传统风险预测模型的优化效果。检验水准α=0.05。
2　结果
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2.1　一般特征比较
共纳入孕妇312例，平均年龄（29.8±3.9）岁，孕前BMI为（22.7±3.4）kg/m2，GDM发生率为22.44%（70例）。与非GDM组相比，GDM组孕前BMI较高［（24.1±3.2）kg/m2 vs. （22.3±3.5）kg/m2，P=0.002］，家族糖尿病史比例更高（27.14% vs. 11.89%，P=0.004）。孕早期体重增长量在GDM组明显增加［（2.8±1.2）kg vs. （1.9±1.0） kg，P<0.001］，孕中期［（6.5±2.1）kg vs. （5.9±2.0） kg，P=0.040］及孕晚期［（4.3±1.7）kg vs. （3.8±1.5） kg，P=0.036］增重亦略高。GDM组中体力活动中高水平比例较低（48.57% vs.69.07%，P=0.006）。年龄、孕次及受教育程度在两组间差异均无统计学意义（P>0.05）。见表1。
2.2　孕期体重增长模式分型结果
LCGA分析将孕期体重增长轨迹划分为三种模式。平稳适宜型占55.13%（172例），中度快速型占31.09%（97例），持续过快型占13.78%（43例）。模型拟合优度指标显示BIC=1867.4、AIC=1821.6、Entropy=0.812，分类精度较高。平稳适宜型孕妇在孕早、中、晚期体重依次增加［（1.8±0.9）、（5.6±1.9）和（3.9±1.4） kg］，总体增重（11.3±2.1） kg；中度快速型总体增重（13.2±2.6） kg，持续过快型总体增重（14.1±2.3） kg。不同模式组间孕前BMI、家族糖尿病史及体力活动水平差异均有统计学意义（P<0.05）。见表2。
2.3　不同体重增长模式的GDM发生率
三种体重增长模式的GDM发生率呈递增趋势：平稳适宜型为14.53%（25/172），中度快速型为29.90%（29/97），持续过快型为37.21%（16/43），组间差异显著（χ2=14.67，P<0.001）。趋势性分析显示，体重增长速度与GDM发生率之间存在线性升高关系（Z=3.76，P<0.001），提示随着体重增长速率升高，GDM发生比例逐步增加。见表3。
2.4　单因素logistic回归分析
单因素回归结果显示，孕前BMI（β=0.116，P=0.005）、家族糖尿病史（β=0.862，P=0.007）、孕早期体重增长（β=0.472，P=0.001）、体力活动水平（β=0.744，P=0.011）及体重增长模式（持续过快 vs. 平稳：β=1.112，P=0.001）与GDM发生显著相关。年龄、吸烟史及孕次差异无统计学意义（P>0.05）。见表4。
2.5　多因素logistic回归分析
在纳入单因素分析P<0.10的变量后建立多因素模型。以不同体重增长模式作为主要暴露因素，在校正了孕前BMI、家族糖尿病史及体力活动水平等潜在混杂因素后，结果显示，持续过快型体重增长（OR=2.95，95%CI：1.54～5.64，P=0.001）、中度快速型（OR=1.68，95%CI：1.02～2.77，P=0.041）、孕前BMI（OR=1.10，95%CI：1.01～1.21，P=0.028）、家族糖尿病史（OR=2.13，95%CI：1.13～4.00，P=0.019）及低体力活动水平（OR=1.89， 95%CI：1.04～3.44，P=0.038）均为独立影响因素。模型拟合良好（Hosmer-Lemeshow χ2=5.32，P=0.537）。见表5。
2.6　模型预测性能分析
模型预测能力分析显示，ROC曲线AUC为0.781（95%CI：0.726～0.835），具有较好的判别效能。在预测概率0.22时，敏感度为72.86%，特异度为73.44%，Youden指数为0.463，表明模型在区分GDM与非GDM孕妇方面表现较好。见表6。进一步分析显示，仅包含孕前BMI、家族糖尿病史和体力活动水平的基础模型AUC为0.712（95%CI：0.651～0.773）。在引入“体重增长模式”后，扩展模型不仅AUC显著提升（Z=2.41，P=0.016），且连续型NRI达到0.465（95%CI：0.218～0.712，P<0.001），IDI为0.068（95%CI：0.025～0.111，P=0.002）。这表明，将体重增长模式纳入传统预测模型中，能显著优化对孕妇GDM发病风险的预测和重新分类能力。
3　讨论
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在本研究中，GDM总体发生率为22.44%，与近年来国内人群研究报道的中高水平接近[9-11]，提示在肥胖患病率上升和生活方式转变背景下，GDM已成为围产保健领域需重点关注的代谢问题。既往系统综述与大样本研究表明，GDM不仅与剖宫产、早产、巨大儿及新生儿监护需求增加等不良围产结局稳定相关，还与产后2型糖尿病及心血管事件风险显著上升有关，对母体及子代均具有长期健康负担[12]。本研究所见患病水平及相关风险梯度，与上述证据在总体趋势上基本一致。本研究基于LCGA识别出平稳适宜型、中度快速型和持续过快型三种孕期体重增长模式，且GDM发生率呈现由14.53%、29.90%至37.21%的递增分布，即使在校正孕前BMI及其他混杂因素后，中度快速型和持续过快型的GDM风险仍分别为平稳适宜型的1.68和2.95倍。这一结果提示，单纯关注孕期总增重可能不足以全面反映代谢风险，不同体重增长轨迹本身具有独立的流行病学意义。国外基于出生队列的研究亦提示，过度增重型轨迹与GDM风险升高相关[13]，而在GDM孕妇中，孕中晚期体重快速上升的模式还与小于胎龄儿、早产及妊娠期高血压等多种不良结局共同相关[14]，从侧面支持了本研究“轨迹模式”视角的合理性。
除体重增长模式外，本研究发现孕前BMI、家族糖尿病史及低体力活动水平均为GDM的独立相关因素，其中孕前BMI每增加1 kg/m2，GDM风险随之上升。既往研究表明，超重和肥胖女性在妊娠期更易出现胰岛素抵抗加重和脂肪因子紊乱，GDM风险随BMI分层呈显著梯度上升。关于体力活动，一系列队列及meta分析证据显示，较高水平孕期体力活动可使GDM风险降低约30%～40%，且第一孕期达到最低推荐活动量即可获得一定获益[15-17]。本研究结果与上述剂量–反应关系方向一致，提示在孕期健康管理中结合体重增长轨迹与体力活动水平，有望更早识别代谢高危人群。生物学层面，既往文献已报道GDM孕妇在脂联素、瘦素、chemerin及炎症因子方面存在不利改变[18-19]，为体重增长过快与糖代谢异常之间的联系提供了可能机制背景。本研究建立的多因素logistic回归模型Hosmer-Lemeshow检验P=0.537，AUC为0.781，敏感度和特异度均在70%以上，表明该模型在研究人群中具有较好的区分能力。与国内基于早孕临床指标构建的列线图模型（AUC约0.72）及部分机器学习模型（AUC约0.80）相比[20-21]，本研究以体重增长轨迹为核心变量，预测效能处于中等偏上水平，提示不同体重增长轨迹与GDM患病状态之间存在显著关联。需要强调的是，本研究旨在探索GDM人群与非GDM人群在整个孕期体重变化模式上的特征差异，受限于评价的时间窗口，其在早期风险预测中的应用价值仍需谨慎解读。
综上，本研究基于前瞻性队列识别出三种孕期体重增长模式，证实中度快速型和持续过快型均与GDM的发生具有显著的相关性。然而，本研究存在一定的局限性：一是，本研究构建的体重增长轨迹包含了孕晚期数据，而GDM的诊断通常在孕中期（24～28周），二者在时间序列上存在重合。因此，本研究所得结论仅为相关性关联，不能推断为因果关系，亦暂不能直接用于早期的GDM风险预测。二是，本研究为单中心研究、样本量有限，体力活动等信息主要来源自报，且未纳入客观饮食、饮酒史与体成分指标，残余混杂难以完全排除。今后可在多地区开展前瞻性研究，仅利用孕早期与中期的体重动态变化数据验证并优化综合预测工具，并评估其在降低GDM发生率方面的实际效果。
基金
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  • 山西省高校科技创新计划项目(2022L672)
文献
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参考文献 引证文献
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2026年第53卷第13期
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doi: 10.20043/j.cnki.MPM.202512073
  • 接收时间：2025-12-07
  • 首发时间：2026-09-28
  • 出版时间：2026-07-10
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  • 收稿日期：2025-12-07
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山西省高校科技创新计划项目(2022L672)
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    1.山西医药学院，山西 汾阳 032200
    2.山西医药学院附属第一医院 介入科
    3.山西医药学院附属第一医院妇产科

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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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