染色体微阵列分析胚胎停育中的遗传学因素及预测模型建立

doi:10.3760/cma.j.cn441417-20250217-14003

国际医药卫生导报 ›› 2025, Vol. 31 ›› Issue (14): 2299-2304.DOI: 10.3760/cma.j.cn441417-20250217-14003

染色体微阵列分析胚胎停育中的遗传学因素及预测模型建立

阎红卫¹ 屈萍² 贾青鸽³ 殷婷²

¹西安高新医院生殖医学科，西安 710075；²西安高新医院产科，西安 710075；³西安国际医学中心医院生殖医学科，西安 710100

收稿日期:2025-02-17 出版日期:2025-07-01 发布日期:2025-08-04
通讯作者: 屈萍，Email：277391994@qq.com
基金资助:
陕西省自然科学基础研究计划（2024JC-YBQN-0834）

Genetic factors and prediction model establishment of chromosome microarray analysis technique in embryo damage

Yan Hongwei¹, Qu Ping², Jia Qingge³, Yin Ting²

¹Department of Reproductive Medicine, Xi'an Hi-tech Hospital, Xi 'an 710075, China; ²Department of Obstetrics, Xi'an Hi-tech Hospital, Xi 'an 710075, China; ³Department of Reproductive Medicine, Xi'an International Medical Center Hospital, Xi'an 710100, China

Received:2025-02-17 Online:2025-07-01 Published:2025-08-04
Contact: Qu Ping, Email: 277391994@qq.com
Supported by:
Basic Research Program of Natural Science in Shaanxi Province (2024JC-YBQN-0834)

摘要/Abstract

摘要：

目的　探究胚胎停育遗传学因素分析中染色体微阵列分析（CMA）技术的应用价值，明确胚胎停育发生的风险因素并建立临床预测模型。方法　选取2023年7月至2024年12于西安高新医院就诊并行清宫术的120例胚胎停育患者，年龄（33.5±1.9）岁，体重指数（23.8±2.2）kg/m²，妊娠次数1～2次者84例、≥3次者36例，人工流产次数无25例、1～2次者79例、≥3次者16例；以同期正常妊娠且自愿要求人工终止妊娠的100例作为对照组，年龄（30.4±2.6）岁，体重指数（24.3±2.5）kg/m²，妊娠次数1～2次者85例、≥3次者15例，人工流产次数无60例、1～2次者33例，≥3次者7例。两组均行流产物绒毛组织CMA，观察胚胎停育患者染色体异常及分布情况。采用χ²、t检验进行统计比较，logistic回归分析影响胚胎停育的相关独立危险因素，并建立胚胎停育风险模型，Hosmer-lemeshow行预测模型拟合度评价，受试者操作特征曲线（ROC）验证模型的诊断价值。结果　观察组120例，CMA检出染色体异常82例（68.3%），其中染色体数目异常75例，结构异常5例，其他异常2例；对照组100例，CMA检出染色体异常3例（3.0%），均为数目异常；观察组染色体异常率明显高于对照组（χ²=98.202，P<0.001）。82例胚胎停育染色体数目异常中，以常染色体三体最为常见（52.4%，43/82），其中发生率较高的依次为16-三体8例（9.8%）、22-三体6例（7.3%）、21-三体5例（6.1%）和15-三体4例（4.9%）；X性染色体异常次之，发生22例（26.8%）。年龄、人工流产次数、不良生活习惯、既往胚胎停育史、血β-HCG水平及染色体异常是引起早孕期胚胎停育的独立危险因素（均P<0.05）。根据二元logistic回归法构建胚胎停育风险预测模型，Hosmer-lemeshow检验模型拟合优度良好；ROC验证预测模型曲线下面积为0.935（0.902～0.978），灵敏度为94.2%，特异度为80.8%，预测价值较高。结论　染色体数目异常是导致胚胎停育的重要原因，CMA技术应用于胚胎停育的遗传学诊断可为患者再生育风险评估提供指导；本研究构建的胚胎停育风险模型具有良好预测价值。

关键词:

染色体微阵列, 胚胎停育, 染色体异常, 预测模型

Abstract:

Objective　To explore the application value of chromosome microarray analysis (CMA) in the analysis of genetic factors of embryo damage, to identify the risk factors of embryo damage, and to establish a clinical prediction model. Methods　One hundred and twenty patients with embryo damage who underwent uterine ablation at Xi'an High-tech Hospital from July 2023 to December 2024 selected; they were (33.5±1.9) years old; their body mass index was (23.8±2.2) kg/m²; 84 cases had 1-2 pregnancies, and 36 cases ≥3 pregnancies; 25 cases had no induced abortion, 79 cases 1-2 times of induced abortion, and 16 cases ≥3 times. One hundred women with normal pregnancy who voluntarily requested artificial termination of pregnancy during the same period were taken as the controls; they were (30.4±2.6) years old; their body mass index was (24.3±2.5) kg/m²; 85 cases had 1-2 pregnancies, and 15 cases ≥3 pregnancies; 60 cases had no induced abortion, 33 cases 1-2 times of induced abortion, and 7 cases ≥3 times. The chromosomal abnormalities and their distribution were observed in both groups by CMA detection of fluidproduct villi tissue. χ² and t tests were used for the statistical comparisons. The logistic regression was used to analyze the related independent risk factors affecting embryo damage, and the risk model of embryo damage was established. Hosmer-lemeshow was used to evaluate the fit degree of the prediction model, and the receiver operating characteristic curve (ROC) was used to verify the diagnostic value of the model. Results　In the observation group, 82 cases (68.3%) of chromosome abnormalities were detected by CMA, including 75 cases of abnormal chromosome number, 5 cases of abnormal structure, and 2 cases of other abnormalities. Three cases (3.0%) of abnormal chromosome number were detected by CMA in the control group. The chromosome abnormality rate in the observation group was significantly higher than that in the control group (χ²=98.202; P<0.001). The most common abnormal number of chromosomes in the embryos was autosomal trisomy (52.4%, 43/82), such as 16-trisomy (8 cases, 9.8%), 22-trisomy (6 cases, 7.3%), 21-trisomy (5 cases, 6.1%), and 15-trisomy (4 cases, 4.9%); X sex chromosome abnormality was the second (22 cases, 26.8%). Age, number of induced abortion, bad living habits, previous history of embryo damage, blood β-HCG level, and chromosome abnormality were the independent risk factors for embryo damage in early pregnancy (all P<0.05). According to the binary logistic regression, the prediction model of embryo damage was constructed. The Hosmer-lemeshow test model had good fit. The area under the curve of the predictive model verified by ROC was 0.935 (0.902-0.978), with a sensitivity of 94.2%, a specificity of 80.8%, and high predictive value. Conclusions　Abnormal chromosome number is an important cause of embryo damage. The application of CMA in genetic diagnosis of embryo damage can provide guidance for the evaluation of reproductive risk of patients. The risk model of embryo damage constructed in this study has good predictive value.

Key words: Chromosome microarray, Embryo damage, Chromosomal abnormalities, Prediction model

阎红卫屈萍贾青鸽殷婷.

染色体微阵列分析胚胎停育中的遗传学因素及预测模型建立 [J]. 国际医药卫生导报, 2025, 31(14): 2299-2304.

Yan Hongwei, Qu Ping, Jia Qingge, Yin Ting.

Genetic factors and prediction model establishment of chromosome microarray analysis technique in embryo damage [J]. International Medicine and Health Guidance News, 2025, 31(14): 2299-2304.

参考文献

[1]Orvieto R, Jonish-Grossman A, Maydan SA, et al. Cleavage-stage human embryo arrest, is it embryo genetic composition or others?[J] Reprod Biol Endocrinol, 2022, 20(1): 52. DOI: 10.1186/s12958-022-00925-2.

[2]李苗苗,江洪,蔡朋达.胚胎停育的影响因素分析及预测研究[J].国际生殖健康/计划生育杂志, 2024, 43(4): 332-337. DOI:10.12280/gjszjk.20240148

[3]巩振华,刘成程,刘娜,等.绒毛组织检测方法的研究进展[J].医学检验与临床, 2019, 30(3): 48-51. DOI:10.3969/j.issn.1673-5013.2019.03.013.

[4]肖艳华,时盼来,李鼎,等.染色体微阵列分析技术在流产或死胎原因分析中的应用[J].中华医学遗传学杂志, 2020, 37(4): 389-391. DOI:10.3760/cma.j.issn.1003-9406.2020.04.006.

[5]Luo X, Zhu H, Wang L, et al. Chromosomal microarray analysis in fetuses with high-risk prenatal indications: a retrospective study in China[J]. Taiwan J Obstet Gynecol, 2021, 60(2): 299-304. DOI: 10.1016/j.tjog.2021.01.008.

[6]Shao L, Akkari Y, Cooley LD, et al Chromosomal microarray analysis, including constitutional and neoplastic disease applications, 2021 revision: a technical standard of the American College of Medical Genetics and Genomics (ACMG)[J]. Genet Med, 2021, 23(10):

1818-1829. DOI: 10.1038/s41436-021-01214-w.

[7]柏卓君,潘勇,李仲余.彩色多普勒超声在早期胚胎停育诊断中的应用[J].影像研究与医学应用, 2020, 4(18): 129-131. DOI:10.3969/j.issn.2096-3807.2020.18.066.

[8]Hamosh A, Amberger JS, Bocchini C, et al. Online Mendelian Inheritance in Man (OMIM®): Victor McKusick's magnum opus[J]. Am J Med Genet A, 2021, 185(11): 3259-3265. DOI: 10.1002/ajmg.a.62407.

[9]Nassar LR, Barber GP, Benet-Pagès A, et al. The UCSC genome browser database: 2023 update[J]. Nucleic Acids Res, 2023, 51(D1): D1188-D1195. DOI: 10.1093/nar/gkac1072.

[10]Pradhan S, Das S, Singh AK, et al. dbGENVOC: database of GENomic Variants of Oral Cancer, with special reference to India[J]. Database (Oxford), 2021, 2021:baab034. DOI: 10.1093/database/baab034.

[11]Firth HV, Richards SM, Bevan AP, et al. DECIPHER: database of chromosomal imbalance and phenotype in humans using ensembl resources[J]. Am J Hum Genet, 2009, 84(4):524-533. DOI: 10.1016/j.ajhg.2009.03.010.

[12]刘宏改,姚俊阁,李想.复发性自然流产与胚胎停育患者染色体异常状况分析[J].中国实用医刊, 2020, 47(18):5. DOI:10.3760/cma.j.cn115689-20200515-02344.

[13]Viotti M, Greco E, Grifo JA, et al. Chromosomal, gestational, and neonatal outcomes of embryos classified as a mosaic by preimplantation genetic testing for aneuploidy[J]. Fertil Steril, 2023, 120(5): 957-966. DOI:10.1016/j.fertnstert.2023.07.022.

[14]Mikhail FM, Biegel JA, Cooley LD, et al.Technical laboratory standards for interpretation and reporting of acquired copy-number abnormalities and copy-neutral loss of heterozygosity in neoplastic disorders:a joint consensus recommendation from the American College of Medical Genetics and Genomics(AC-MG) and the Cancer Genomics Consortium(CGC)[J]. GenetMed, 2019, 21(91): 1903-1916. DOI: 10.1038/s41436-019-0545-7.

[15]王珺,陈书强,高玲霞,等. 1317例早期流产患者绒毛染色体检测结果分析[J].生殖医学杂志, 2019, 28(1): 7-11. DOI:10.3969/j.issn.1004-3845.2019.01.002.

[16]刘海珍,邵小光,孙秀静.早期自然流产患者绒毛染色体检测结果分析[J].华南预防医学, 2021, 47(12): 1572-1574. DOI:10.12183/j.scjpm.2021.1572.

[17]王威,王昕,管晓娇,等.孕早期胚胎停育的绒毛染色体核型异常分析[J].中国医刊, 2021, 56(2): 204-206. DOI:10.3969/j.issn.1008-1070.2021.02.026.

[18]徐万洲,李锋,周方元,等.染色体G显带核型分析联合微阵列分析技术诊断罕见9p四体嵌合体1例[J].临床检验杂志, 2024, 42(2): 156-158. DOI:10.13602/j.cnki.jcls.2024.02.16.

[19]尹婷,王志伟,王永安,等.基于染色体微阵列分析技术对胚胎停育患者流产绒毛组织遗传因素的研究[J].中国妇幼保健, 2019, 34(16): 3733-3735. DOI:10.7620/zgfybj.j.issn.1001-4411.2019.16.34.

[20]赵炜,梁思颖,谢宁,等.染色体核型分析联合染色体微阵列分析在平衡易位/倒位携带者产前诊断中的应用价值[J].中华围产医学杂志, 2022, 25(1): 35-41. DOI:10.3760/cma.j.cn113903-20210430-00403.

[21]谭满胜,李祝坤,唐玉芬,等.染色体核型分析联合染色体微阵列分析技术在产前诊断中的应用价值[J].检验医学与临床, 2022, 19(15): 2108-2111. DOI:10.3969/j.issn.1672-9455.2022.15.022.

[22]张琪,邹前健.胚胎停育患者胚胎染色体异常分布情况及其相关危险因素分析[J].中国当代医药, 2024, 31(36): 92-95. DOI:10.3969/j.issn.1674-4721.2024.36.023.

[23]冯小月,陈利侠,吴惠芹,等.妊娠早期胚胎停育的高危因素分析[J].中国药物与临床, 2021, 21(23): 3852-3855. DOI:10.11655/zgywylc2021.23.018.

[24]祁冰.探讨早孕胚胎停育与多种接触因素相关性和胚胎停育的病因[J/OL].实用妇科内分泌电子杂志, 2019, 6(28): 24,28[2025-01-01]. https://kns.cnki.net/kcms2/article/abstract?v=uTZa6doZ_i5VECPFp_EHmF9dEe0m25KLDw2gbgSYuOQJyReVJmY_P8I3WHPE0oDeT_PNf3x-bCqTqodl-NQiMm5CyPlolErsGxXrhAph0-UENBOo3bz0NF0-IME5nUpbU4hVpZ6MM8pG1wq-YgZtwwgbyV2sEOW11CzPMo22C5OARhtwAhYjzQ==&uniplatform=NZKPT&language=CHS.DOI:10.16484/j.cnki.issn2095-8803.2019.28.015.

[25]刘海燕,李钰玲.孕早期血清HCG、E2、P水平在胚胎停育中诊断价值的研究进展[J].中国当代医药, 2020, 27(29): 25-28. DOI:10.3969/j.issn.1674-4721.2020.29.008.

染色体微阵列分析胚胎停育中的遗传学因素及预测模型建立

Genetic factors and prediction model establishment of chromosome microarray analysis technique in embryo damage

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