Abstract

Type 2 diabetes mellitus (DM2) is one of the most widely prevalent diseases worldwide and is currently screened by invasive techniques based on enzymatic assays that measure plasma glucose concentration in a laboratory setting. A promising plan of action for screening DM2 is to identify molecular signatures in a non-invasive fashion. This work describes the application of portable Raman spectroscopy coupled with several supervised machine-learning techniques, to discern between diabetic patients and healthy controls (Ctrl), with a high degree of accuracy. Using artificial neural networks (ANN), we accurately discriminated between DM2 and Ctrl groups with 88.9–90.9% accuracy, depending on the sampling site. In order to compare the ANN performance to more traditional methods used in spectroscopy, principal component analysis (PCA) was carried out. A subset of features from PCA was used to generate a support vector machine (SVM) model, albeit with decreased accuracy (76.0–82.5%). The 10-fold cross-validation model was performed to validate both classifiers. This technique is relatively low-cost, harmless, simple and comfortable for the patient, yielding rapid diagnosis. Furthermore, the performance of the ANN-based method was better than the typical performance of the invasive measurement of capillary blood glucose. These characteristics make our method a promising screening tool for identifying DM2 in a non-invasive and automated fashion.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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References

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2017 (2)

N. Chiles Shaffer, L. Ferrucci, M. Shardell, E. M. Simonsick, and S. Studenski, “Agreement and Predictive Validity Using Less-Conservative Foundation for the National Institutes of Health Sarcopenia Project Weakness Cutpoints,” J. Am. Geriatr. Soc. 65(3), 574–579 (2017).
[Crossref] [PubMed]

J. Cheng, L. Xu, G. Lü, J. Tang, J. Mo, X. Lü, and Z. Gao, “Study on the echinococcosis blood serum detection based on Raman spectroscopy combined with neural network,” Optoelectron. Lett. 13(1), 77–80 (2017).
[Crossref]

2016 (3)

M. Jermyn, J. Desroches, J. Mercier, M.-A. Tremblay, K. St-Arnaud, M.-C. Guiot, K. Petrecca, and F. Leblond, “Neural networks improve brain cancer detection with Raman spectroscopy in the presence of operating room light artifacts,” J. Biomed. Opt. 21(9), 094002 (2016).
[Crossref] [PubMed]

J. F. Villa-Manríquez, J. Castro-Ramos, F. Gutiérrez-Delgado, M. A. Lopéz-Pacheco, and A. E. Villanueva-Luna, “Raman spectroscopy and PCA-SVM as a non-invasive diagnostic tool to identify and classify qualitatively glycated hemoglobin levels in vivo,” J. Biophotonics 10(8), 1074–1079 (2016).
[PubMed]

S. Khan, R. Ullah, A. Khan, N. Wahab, M. Bilal, and M. Ahmed, “Analysis of dengue infection based on Raman spectroscopy and support vector machine (SVM),” Biomed. Opt. Express 7(6), 2249–2256 (2016).
[Crossref] [PubMed]

2015 (7)

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis. 115(3), 211–252 (2015).
[Crossref]

B. Yan, B. Li, Z. Wen, X. Luo, L. Xue, and L. Li, “Label-free blood serum detection by using surface-enhanced Raman spectroscopy and support vector machine for the preoperative diagnosis of parotid gland tumors,” BMC Cancer 15(1), 650 (2015).
[Crossref] [PubMed]

E. Combrisson and K. Jerbi, “Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy,” J. Neurosci. Methods 250, 126–136 (2015).
[Crossref] [PubMed]

L. Franzen and M. Windbergs, “Applications of Raman spectroscopy in skin research--From skin physiology and diagnosis up to risk assessment and dermal drug delivery,” Adv. Drug Deliv. Rev. 89, 91–104 (2015).
[Crossref] [PubMed]

L. Pereira, C. A. T. Soto, L. D. Santos, P. P. Favero, and A. A. Martin, “Confocal Raman Spectroscopy as an Optical Sensor to Detect Advanced Glycation End Products of the Skin Dermis,” Sens. Lett. 13(9), 791–801 (2015).
[Crossref]

J. M. Ashraf, S. Ahmad, G. Rabbani, Q. Hasan, A. T. Jan, E. J. Lee, R. H. Khan, K. Alam, and I. Choi, “3-Deoxyglucosone: A Potential Glycating Agent Accountable for Structural Alteration in H3 Histone Protein Through Generation of Different AGEs,” PLoS One 10(2), e0116804 (2015).
[Crossref] [PubMed]

I. Allaman, M. Bélanger, and P. J. Magistretti, “Methylglyoxal, the dark side of glycolysis,” Front. Neurosci. 9, 23 (2015).
[Crossref] [PubMed]

2014 (5)

V. P. Singh, A. Bali, N. Singh, and A. S. Jaggi, “Advanced Glycation End Products and Diabetic Complications,” Korean J. Physiol. Pharmacol. 18(1), 1–14 (2014).
[Crossref] [PubMed]

A. Di Martino, C.-G. Yan, Q. Li, E. Denio, F. X. Castellanos, K. Alaerts, J. S. Anderson, M. Assaf, S. Y. Bookheimer, M. Dapretto, B. Deen, S. Delmonte, I. Dinstein, B. Ertl-Wagner, D. A. Fair, L. Gallagher, D. P. Kennedy, C. L. Keown, C. Keysers, J. E. Lainhart, C. Lord, B. Luna, V. Menon, N. J. Minshew, C. S. Monk, S. Mueller, R.-A. Müller, M. B. Nebel, J. T. Nigg, K. O’Hearn, K. A. Pelphrey, S. J. Peltier, J. D. Rudie, S. Sunaert, M. Thioux, J. M. Tyszka, L. Q. Uddin, J. S. Verhoeven, N. Wenderoth, J. L. Wiggins, S. H. Mostofsky, and M. P. Milham, “The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism,” Mol. Psychiatry 19(6), 659–667 (2014).
[Crossref] [PubMed]

J. Beagley, L. Guariguata, C. Weil, and A. A. Motala, “Global estimates of undiagnosed diabetes in adults,” Diabetes Res. Clin. Pract. 103(2), 150–160 (2014).
[Crossref] [PubMed]

C. Koushik, S. Anuj, S. Neeraj, and S. Shiru, “Estimation of fasting Blood glucose levels by invasive and indigenously developed noninvasive technology and its correlation with the glycated hemoglobin (HbA1c) biomarker in healthy and diabetic subjects,” Res. J. Biotechnol. 9, 61–68 (2014).

S. Li, Y. Zhang, J. Xu, L. Li, Q. Zeng, L. Lin, Z. Guo, Z. Liu, H. Xiong, and S. Liu, “Noninvasive prostate cancer screening based on serum surface-enhanced Raman spectroscopy and support vector machine,” Appl. Phys. Lett. 105(9), 091104 (2014).
[Crossref]

2013 (3)

I. J. Pence, E. Vargis, and A. Mahadevan-Jansen, “Assessing variability of in vivo tissue Raman spectra,” Appl. Spectrosc. 67(7), 789–800 (2013).
[Crossref] [PubMed]

R. T. Demmer, A. M. Zuk, M. Rosenbaum, and M. Desvarieux, “Prevalence of diagnosed and undiagnosed type 2 diabetes mellitus among US adolescents: results from the continuous NHANES, 1999-2010,” Am. J. Epidemiol. 178(7), 1106–1113 (2013).
[Crossref] [PubMed]

X. Zhao, W. Zhao, H. Zhang, J. Li, Y. Shu, S. Li, L. Cai, J. Zhou, Y. Li, and R. Hu, “Fasting capillary blood glucose: an appropriate measurement in screening for diabetes and pre-diabetes in low-resource rural settings,” J. Endocrinol. Invest. 36(1), 33–37 (2013).
[PubMed]

2012 (3)

J. Alda, C. Castillo-Martinez, R. Valdes-Rodriguez, D. Hernández-Blanco, B. Moncada, and F. J. González, “Use of Raman spectroscopy in the analysis of nickel allergy,” J. Biomed. Opt. 18(6), 061206 (2012).
[Crossref] [PubMed]

N. C. Dingari, G. L. Horowitz, J. W. Kang, R. R. Dasari, and I. Barman, “Raman spectroscopy provides a powerful diagnostic tool for accurate determination of albumin glycation,” PLoS One 7(2), e32406 (2012).
[Crossref] [PubMed]

P. Gaspar, J. Carbonell, and J. L. Oliveira, “On the parameter optimization of Support Vector Machines for binary classification,” J. Integr. Bioinform. 9(3), 201 (2012).
[Crossref] [PubMed]

2011 (1)

F. J. González, J. Alda, B. Moreno-Cruz, M. Martínez-Escanamé, M. G. Ramírez-Elías, B. Torres-Álvarez, and B. Moncada, “Use of Raman spectroscopy for the early detection of filaggrin-related atopic dermatitis,” Skin Res. Technol. 17(1), 45–50 (2011).
[Crossref] [PubMed]

2010 (4)

S. Thorvaldsen, T. Flå, and N. P. Willassen, “DeltaProt: a software toolbox for comparative genomics,” BMC Bioinformatics 11(1), 573 (2010).
[Crossref] [PubMed]

E. Guevara and F. J. González, “Joint optical-electrical technique for noninvasive glucose monitoring,” Rev. Mex. Fis. 56, 430–434 (2010).

J. R. Beattie, A. M. Pawlak, M. E. Boulton, J. Zhang, V. M. Monnier, J. J. McGarvey, and A. W. Stitt, “Multiplex analysis of age-related protein and lipid modifications in human Bruch’s membrane,” FASEB J. 24(12), 4816–4824 (2010).
[Crossref] [PubMed]

M. Sattlecker, C. Bessant, J. Smith, and N. Stone, “Investigation of support vector machines and Raman spectroscopy for lymph node diagnostics,” Analyst (Lond.) 135(5), 895–901 (2010).
[Crossref] [PubMed]

2008 (2)

A. M. Pawlak, J. V. Glenn, J. R. Beattie, J. J. McGarvey, and A. W. Stitt, “Advanced glycation as a basis for understanding retinal aging and noninvasive risk prediction,” Ann. N. Y. Acad. Sci. 1126(1), 59–65 (2008).
[Crossref] [PubMed]

R. Meerwaldt, T. Links, C. Zeebregts, R. Tio, J.-L. Hillebrands, and A. Smit, “The clinical relevance of assessing advanced glycation endproducts accumulation in diabetes,” Cardiovasc. Diabetol. 7(1), 29 (2008).
[Crossref] [PubMed]

2007 (2)

2004 (2)

N. Shangari and P. J. O’Brien, “The cytotoxic mechanism of glyoxal involves oxidative stress,” Biochem. Pharmacol. 68(7), 1433–1442 (2004).
[Crossref] [PubMed]

M. Gniadecka, P. A. Philipsen, S. Sigurdsson, S. Wessel, O. F. Nielsen, D. H. Christensen, J. Hercogova, K. Rossen, H. K. Thomsen, R. Gniadecki, L. K. Hansen, and H. C. Wulf, “Melanoma diagnosis by Raman spectroscopy and neural networks: structure alterations in proteins and lipids in intact cancer tissue,” J. Invest. Dermatol. 122(2), 443–449 (2004).
[Crossref] [PubMed]

2003 (1)

G.-B. Huang, “Learning capability and storage capacity of two-hidden-layer feedforward networks,” IEEE Trans. Neural Netw. 14(2), 274–281 (2003).
[Crossref] [PubMed]

2002 (2)

J.-O. Jeppsson, U. Kobold, J. Barr, A. Finke, W. Hoelzel, T. Hoshino, K. Miedema, A. Mosca, P. Mauri, R. Paroni, L. Thienpont, M. Umemoto, C. Weykamp, and International Federation of Clinical Chemistry and Laboratory Medicine (IFCC), “Approved IFCC reference method for the measurement of HbA1c in human blood,” Clin. Chem. Lab. Med. 40(1), 78–89 (2002).
[Crossref] [PubMed]

J. M. López-Alonso, J. Alda, and E. Bernabéu, “Principal-component characterization of noise for infrared images,” Appl. Opt. 41(2), 320–331 (2002).
[Crossref] [PubMed]

2000 (2)

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1968 (1)

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Y. Benjamini and Y. Hochberg, “Controlling the false discovery rate: a practical and powerful approach to multiple testing,” J. R. Stat. Soc. Ser. B Methodol. 57, 289–300 (1995).

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N. C. Dingari, G. L. Horowitz, J. W. Kang, R. R. Dasari, and I. Barman, “Raman spectroscopy provides a powerful diagnostic tool for accurate determination of albumin glycation,” PLoS One 7(2), e32406 (2012).
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Deen, B.

A. Di Martino, C.-G. Yan, Q. Li, E. Denio, F. X. Castellanos, K. Alaerts, J. S. Anderson, M. Assaf, S. Y. Bookheimer, M. Dapretto, B. Deen, S. Delmonte, I. Dinstein, B. Ertl-Wagner, D. A. Fair, L. Gallagher, D. P. Kennedy, C. L. Keown, C. Keysers, J. E. Lainhart, C. Lord, B. Luna, V. Menon, N. J. Minshew, C. S. Monk, S. Mueller, R.-A. Müller, M. B. Nebel, J. T. Nigg, K. O’Hearn, K. A. Pelphrey, S. J. Peltier, J. D. Rudie, S. Sunaert, M. Thioux, J. M. Tyszka, L. Q. Uddin, J. S. Verhoeven, N. Wenderoth, J. L. Wiggins, S. H. Mostofsky, and M. P. Milham, “The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism,” Mol. Psychiatry 19(6), 659–667 (2014).
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Delmonte, S.

A. Di Martino, C.-G. Yan, Q. Li, E. Denio, F. X. Castellanos, K. Alaerts, J. S. Anderson, M. Assaf, S. Y. Bookheimer, M. Dapretto, B. Deen, S. Delmonte, I. Dinstein, B. Ertl-Wagner, D. A. Fair, L. Gallagher, D. P. Kennedy, C. L. Keown, C. Keysers, J. E. Lainhart, C. Lord, B. Luna, V. Menon, N. J. Minshew, C. S. Monk, S. Mueller, R.-A. Müller, M. B. Nebel, J. T. Nigg, K. O’Hearn, K. A. Pelphrey, S. J. Peltier, J. D. Rudie, S. Sunaert, M. Thioux, J. M. Tyszka, L. Q. Uddin, J. S. Verhoeven, N. Wenderoth, J. L. Wiggins, S. H. Mostofsky, and M. P. Milham, “The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism,” Mol. Psychiatry 19(6), 659–667 (2014).
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O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis. 115(3), 211–252 (2015).
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R. T. Demmer, A. M. Zuk, M. Rosenbaum, and M. Desvarieux, “Prevalence of diagnosed and undiagnosed type 2 diabetes mellitus among US adolescents: results from the continuous NHANES, 1999-2010,” Am. J. Epidemiol. 178(7), 1106–1113 (2013).
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A. Di Martino, C.-G. Yan, Q. Li, E. Denio, F. X. Castellanos, K. Alaerts, J. S. Anderson, M. Assaf, S. Y. Bookheimer, M. Dapretto, B. Deen, S. Delmonte, I. Dinstein, B. Ertl-Wagner, D. A. Fair, L. Gallagher, D. P. Kennedy, C. L. Keown, C. Keysers, J. E. Lainhart, C. Lord, B. Luna, V. Menon, N. J. Minshew, C. S. Monk, S. Mueller, R.-A. Müller, M. B. Nebel, J. T. Nigg, K. O’Hearn, K. A. Pelphrey, S. J. Peltier, J. D. Rudie, S. Sunaert, M. Thioux, J. M. Tyszka, L. Q. Uddin, J. S. Verhoeven, N. Wenderoth, J. L. Wiggins, S. H. Mostofsky, and M. P. Milham, “The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism,” Mol. Psychiatry 19(6), 659–667 (2014).
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N. C. Dingari, G. L. Horowitz, J. W. Kang, R. R. Dasari, and I. Barman, “Raman spectroscopy provides a powerful diagnostic tool for accurate determination of albumin glycation,” PLoS One 7(2), e32406 (2012).
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Dinstein, I.

A. Di Martino, C.-G. Yan, Q. Li, E. Denio, F. X. Castellanos, K. Alaerts, J. S. Anderson, M. Assaf, S. Y. Bookheimer, M. Dapretto, B. Deen, S. Delmonte, I. Dinstein, B. Ertl-Wagner, D. A. Fair, L. Gallagher, D. P. Kennedy, C. L. Keown, C. Keysers, J. E. Lainhart, C. Lord, B. Luna, V. Menon, N. J. Minshew, C. S. Monk, S. Mueller, R.-A. Müller, M. B. Nebel, J. T. Nigg, K. O’Hearn, K. A. Pelphrey, S. J. Peltier, J. D. Rudie, S. Sunaert, M. Thioux, J. M. Tyszka, L. Q. Uddin, J. S. Verhoeven, N. Wenderoth, J. L. Wiggins, S. H. Mostofsky, and M. P. Milham, “The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism,” Mol. Psychiatry 19(6), 659–667 (2014).
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M. I. Harris and R. C. Eastman, “Early detection of undiagnosed diabetes mellitus: a US perspective,” Diabetes Metab. Res. Rev. 16(4), 230–236 (2000).
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Figures (6)

Fig. 1
Fig. 1 Images of skin sites for in vivo Raman spectra acquisition: (A) ear lobe, (B) inner arm (C) thumb nail (D) median cubital vein. Also shown at the right side are the corresponding Raman measurements (mean ± standard deviation) acquired at an excitation wavelength of 785nm (E-H), where control spectra are displayed in blue, whereas DM2 spectra are shown in red.
Fig. 2
Fig. 2 Schematic diagrams of the supervised classifiers: (A) artificial neural network (ANN) architecture (B) support vector machine (SVM) structure (C) Cumulative percentage of total variation as explained by each principal component.
Fig. 3
Fig. 3 (A) ROC curve of the proposed ANN (B) Performance metrics of the ANN for various sampling sites, dotted line at 70% represents the minimum accuracy to assert statistical significance (C) ROC curve of the SVM classifier with linear kernel (D) Performance metrics of the SVM classifier with linear kernel, dotted line at 70% represents the minimum accuracy to assert statistical significance
Fig. 4
Fig. 4 Heatmaps of the Pearson’s correlation mapping (absolute value) between the AGEs spectra (bottom) and the scores of principal components of the patients’ data(left axis) at different sampling sites: A) Ear lobe, B) Inner arm, C) Thumbnail and D) Cubital vein. Correlations have been FDR-corrected.
Fig. 5
Fig. 5 Examples of SVM classification on a bi-dimensional space from different skin sites: (A) ear lobe (B) inner arm (C) thumbnail (D) cubital vein
Fig. 6
Fig. 6 (A) ROC curve of the SVM classifier with RBF kernel (B) Performance metrics of the SVM classifier with RBF kernel, dotted line at 70% represents the minimum accuracy to assert statistical significance.

Tables (1)

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Table 1 Comparison of the average area under the curve (AUC ± 95% confidence intervals) of both classifiers

Equations (7)

Equations on this page are rendered with MathJax. Learn more.

Se= TP TP+FN .
Sp= TN TN+FP .
Gm= SeSp .
PPV= TP TP+FP .
NPV= TN TN+FN .
Fm= 2PPVSe PPV+Se .
Acc= TP+TN TP+FP+TN+FN .

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