拉曼光谱在妊娠糖尿病诊断中的应用价值

doi:10.3969/j.issn.1673-8640.2021.03.017

摘要/Abstract

摘要：

目的探讨拉曼光谱在妊娠糖尿病（GDM）中的应用价值。方法选取29名非孕体检健康者（正常对照组）、29例正常妊娠妇女（正常妊娠组）和42例GDM患者,建立拉曼光谱诊断GDM的方法。另选取78例GDM患者和70名体检健康者进行方法验证。采用正交偏最小二乘回归（OPLS）、主成分分析（PCA）-支持向量机（SVM）及PCA-隐含狄利克雷分布（LDA）3种数学模型对拉曼光谱结果进行分类,并筛选出特征峰。采用受试者工作特征（ROC）曲线评价3种模型的分类能力,筛选最优模型,并与口服葡萄糖耐量试验（OGTT）进行比较。结果 GDM患者、正常对照者及正常妊娠妇女在1 005.86、1 034.47、1 087.64和1 128.39 cm ^-1处均出现较明显的拉曼光谱峰。GDM组在1 005.86、1 034.47、1 128.39和1 343.69 cm ^-1处的拉曼光谱峰与正常对照组及正常妊娠组有明显差异。OPLS模型对GDM组、正常对照组及正常妊娠组的区分能力最佳。采用OPLS模型区分拉曼光谱诊断GDM的敏感性为97.43%、准确度为95.27%,高于OGTT法的91.03%和93.92%;但特异性为92.86%,低于OGTT法的97.14%。结论拉曼光谱结合OPLS模型具有较好的敏感性、特异性和准确度,或可作为GDM筛查和诊断的有效方法。

关键词: 拉曼光谱, 妊娠糖尿病, 多元分析, 快速筛查

Abstract:

Objective To investigate the role of Raman spectroscopy in gestational diabetes mellitus（GDM）. Methods A total of 29 non-pregnant healthy subjects,29 pregnant healthy subjects and 42 GDM patients were enrolled. Meanwhile,78 GDM patients and 70 healthy subjects were enrolled for verification. Orthogonal partial least square regression（OPLS）,principal component analysis（PCA） based method support vector machine（SVM） and latent dirichlet allocation（LDA） were used to classify Raman spectra obtained. Receiver operating characteristic（ROC）curve was used to evaluate the classification ability of the 3 models,and the optimal model was selected. The new method was compared with oral glucose tolerance test（OGTT）. Results Raman peaks appeared at 1 005.86,1 034.47,1 087.64 and 1 128.39 cm^-1 in the GDM group,non-pregnant healthy group and pregnant healthy group,respectively. The Roman peaks in the GDM group at 1 005.86,1 034.47,1 128.39 and 1 343.69 cm^-1 were significantly different from that in non-pregnant healthy group and pregnant healthy group. OPLS model had a good ability to distinguish GDM,non-pregnant healthy and pregnant healthy groups. The sensitivity and accuracy of OPLS model to distinguish GDM by Raman spectroscopy were 97.43% and 95.27%,respectively,which were higher than OGTT（91.03% and 93.92%）. However,the specificity of OPLS was lower than that of OGTT（92.86% vs 97.14%）. Conclusions Raman spectroscopy combined with OPLS model has high sensitivity,specificity and accuracy,It may be used for rapid screening of GDM.

Key words: Raman spectroscopy, Gestational diabetes mellitus, Multivariate analysis, Rapid screening

中图分类号:

R446.1

周丽芳, 潘顺, 唐振华. 拉曼光谱在妊娠糖尿病诊断中的应用价值[J]. 检验医学, 2021, 36(3): 313-317.

ZHOU Lifang, PAN Shun, TANG Zhenhua. Application of Raman spectroscopy in the diagnosis of gestational diabetes mellitus[J]. Laboratory Medicine, 2021, 36(3): 313-317.

图/表 7

参考文献 15

[1]	CAISSUTTI C, BERGHELLA V. Scientific evidence for different options for GDM screening and management:controversies and review of the literature[J]. Biomed Res Int, 2017,2017:2746471. DOI URL PMID
[2]	BERGER H, SERMER M. Counterpoint:selective screening for gestational diabetes mellitus[J]. Diabetes Care, 2009,32(7):1352-1354. URL PMID
[3]	ESMONDE-WHITE K A, CUELLAR M, UERPMANN C, et al. Raman spectroscopy as a process analytical technology for pharmaceutical manufacturing and bioprocessing[J]. Anal Bioanal Chem, 2017,409(3):637-649. DOI URL PMID
[4]	PANDEY R, PAIDI S K, VALDEZ T A, et al. Noninvasive monitoring of blood glucose with raman spectroscopy[J]. Acc Chem Res, 2017,50(2):264-272. DOI URL PMID
[5]	International Association of the Diabetes and Pregnancy Study Groups Consensus Panel METZGER B E, GABBE S G, et al. International association of diabetes and pregnancy study groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy[J]. Diabetes Care, 2010,33(3):676-682. DOI URL PMID
[6]	DE GELDER J, DE GUSSEM K, VANDENABEELE P, et al. Reference database of Raman spectra of biological molecules[J]. J Raman Spectrosc, 2007,38:1133-1147.
[7]	GAWINKOWSKI S, PSZONA M, GORSKI A, et al. Single molecule Raman spectra of porphycene isotopologues[J]. Nanoscale, 2016,8(6):3337-3349.
[8]	FENG Y, ZHOU J, DU Y, et al. Raman spectra of few-layer phosphorene studied from first-principles calculations[J]. J Phys Condens Matter, 2015,27(18):185302. DOI URL PMID
[9]	CHEN S, LIN X, YUEN C, et al. Recovery of Raman spectra with low signal-to-noise ratio using Wiener estimation[J]. Opt Express, 2014,22(10):12102-12114. DOI URL PMID
[10]	NEELAMRAJU S, BACH A SCHÖN J C, et al. Experimental and theoretical study on Raman spectra of magnesium fluoride clusers and solids[J]. J Chem Phys, 2012,137(19):194319. URL PMID
[11]	HENRY C M. Raman spectra of breast tissue[J]. Anal Chem, 1996,68(23):718A-719A. URL PMID
[12]	MACERNIS M, SULSKUS J, MALICKAJA S. Resonance Raman spectra and electronic transitions in carotenoids:a density functional theory study[J]. J Phys Chem A, 2014,118(10):1817-1825. URL PMID
[13]	熊义, 赵虹霞, 干福喜. 阳离子对硅酸盐玻璃激光拉曼光谱影响的研究[J]. 光谱学与光谱分析, 2012,32(4):997-1001.
[14]	LIN J, HUANG Z, FENG S, et al. Label-free optical detection of type Ⅱ diabetes based on surface-enhanced Raman spectroscopy and multivariate analysis[J]. J Raman Spectrosc, 2014,45(10):884-889.
[15]	BIRECH Z, MWANGI P W, BUKACHI F, et al. Application of Raman spectroscopy in type 2 diabetes screening in blood using leucine and isoleucine amino-acids as biomarkers and in comparative anti-diabetic drugs efficacy studies[J]. PLoS One, 2017,12(9):e0185130.

正常对照组/cm^-1	正常妊娠组/cm^-1	GDM组/cm^-1	归属指认
856.61	856.61	856.61	酪氨酸、苯环
1 005.86	1 005.86	1 005.86	β-D-葡萄糖、乳糖、N-乙酰-D-葡萄糖胺、乙酰乙酸、β-胡萝卜素
1 034.47	1 034.47	1 034.47	乳糖、D-（+）-甘露糖、D-（+）-岩藻糖、D-（+）-半乳糖胺、磷酸烯醇式丙酮酸
1 087.64	1 087.64	1 087.64	乳糖、直链淀粉、支链淀粉、 D-（+）-甘露糖、 D-（+）-岩藻糖、D-（+）-木糖、 D-（+）-果糖、N-乙酰-D-葡萄糖胺、柠檬酸、琥珀酸、乙酰乙酸
1 128.39	1 128.39	1 128.39	直链淀粉、支链淀粉、 D-（+）-岩藻糖、N-乙酰-D-葡萄糖胺、D-果糖-6-磷酸
1 157.00	1 159.66	1 159.66	D-（+）-岩藻糖、 D-（+）-半乳糖胺、辅酶、乙酰乙酸、β-胡萝卜素、核黄素
1 213.51	1 213.51	1 213.51	酪氨酸、苯丙氨酸、酰胺Ⅲ、胸腺嘧啶、L-苯丙氨酸、 D-（+）-海藻糖、柠檬酸
		1 343.69	直链淀粉、D-（+）-木糖、柠檬酸、D-果糖-6-磷酸、核黄素
1 445.10	1 445.87	1 442.32	硬脂酸、三烯醇、壳丁、磷酸酚丙酮、醋酸、谷胱甘肽
		1 523.06	β-胡萝卜素
1 655.00	1 657.00	1 657.00	丙酮酸、琥珀酸、几丁质、曲安奈德、三油酸、乙酸、油酸、14-甲基十五烷酸、肉豆蔻酸、月桂酸

正常对照组/cm^-1	正常妊娠组/cm^-1	GDM组/cm^-1	归属指认
856.61	856.61	856.61	酪氨酸、苯环
1 005.86	1 005.86	1 005.86	β-D-葡萄糖、乳糖、N-乙酰-D-葡萄糖胺、乙酰乙酸、β-胡萝卜素
1 034.47	1 034.47	1 034.47	乳糖、D-（+）-甘露糖、D-（+）-岩藻糖、D-（+）-半乳糖胺、磷酸烯醇式丙酮酸
1 087.64	1 087.64	1 087.64	乳糖、直链淀粉、支链淀粉、 D-（+）-甘露糖、 D-（+）-岩藻糖、D-（+）-木糖、 D-（+）-果糖、N-乙酰-D-葡萄糖胺、柠檬酸、琥珀酸、乙酰乙酸
1 128.39	1 128.39	1 128.39	直链淀粉、支链淀粉、 D-（+）-岩藻糖、N-乙酰-D-葡萄糖胺、D-果糖-6-磷酸
1 157.00	1 159.66	1 159.66	D-（+）-岩藻糖、 D-（+）-半乳糖胺、辅酶、乙酰乙酸、β-胡萝卜素、核黄素
1 213.51	1 213.51	1 213.51	酪氨酸、苯丙氨酸、酰胺Ⅲ、胸腺嘧啶、L-苯丙氨酸、 D-（+）-海藻糖、柠檬酸
		1 343.69	直链淀粉、D-（+）-木糖、柠檬酸、D-果糖-6-磷酸、核黄素
1 445.10	1 445.87	1 442.32	硬脂酸、三烯醇、壳丁、磷酸酚丙酮、醋酸、谷胱甘肽
		1 523.06	β-胡萝卜素
1 655.00	1 657.00	1 657.00	丙酮酸、琥珀酸、几丁质、曲安奈德、三油酸、乙酸、油酸、14-甲基十五烷酸、肉豆蔻酸、月桂酸

模型	准确度	敏感性	特异性	阳性预测值	阴性预测值
PCA-LDA	91.16	93.00	88.50	92.98	92.00
PCA-SVM	90.34	91.33	88.00	92.29	91.81
OPLS	96.09	100.00	90.50	94.29	100.00

模型	准确度	敏感性	特异性	阳性预测值	阴性预测值
PCA-LDA	91.16	93.00	88.50	92.98	92.00
PCA-SVM	90.34	91.33	88.00	92.29	91.81
OPLS	96.09	100.00	90.50	94.29	100.00