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Biomedical Optics Express

Biomedical Optics Express

  • Editor: Joseph A. Izatt
  • Vol. 5, Iss. 4 — Apr. 1, 2014
  • pp: 990–999
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Continuous noninvasive monitoring of changes in human skin optical properties during oral intake of different sugars with optical coherence tomography

Yuqing Zhang, Guoyong Wu, Huajiang Wei, Zhouyi Guo, Hongqin Yang, Yonghong He, Shusen Xie, and Ying Liu  »View Author Affiliations


Biomedical Optics Express, Vol. 5, Issue 4, pp. 990-999 (2014)
http://dx.doi.org/10.1364/BOE.5.000990


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Abstract

The objective of this study was to evaluate the effects of blood glucose concentration (BGC) on in vivo human skin optical properties after oral intake of different sugars. In vivo optical properties of human skin were measured with a spectral domain optical coherence tomography (SD-OCT). Experimental results show that increase of BGC causes a decrease in the skin attenuation coefficient. And the maximum decrements in mean attenuation coefficient of skin tissue after drinking glucose, sucrose and fructose solution are 47.0%, 36.4% and 16.5% compared with that after drinking water, respectively (p < 0.05). The results also show that blood glucose levels of the forearm skin tissue are delayed compared with finger-stick blood glucose, and there are significant differences in the time delays after oral intake of different sugars. The time delay between mean attenuation coefficient and BGC after drinking glucose solution is evidently larger than that after drinking sucrose solution, and that after drinking sucrose solution is larger than that after drinking fructose solution. Our pilot studies indicate that OCT technique is capable of non-invasive, real-time, and sensitive monitoring of skin optical properties in human subjects during oral intake of different sugars.

© 2014 Optical Society of America

1. Introduction

Diabetes mellitus, often referred to as diabetes is a group of metabolic diseases in which a person has high blood sugar. Diabetes is widespread nowadays. Over 300 million people in the world are affected [1

1. X. X. Guo, A. Mandelis, and B. Zinman, “Noninvasive glucose detection in human skin using wavelength modulated differential laser photothermal radiometry,” Biomed. Opt. Express 3(11), 3012–3021 (2012). [CrossRef] [PubMed]

]. Currently, there are a few dozen commercialized devices for detecting blood glucose levels [2

2. S. Wild, G. Roglic, A. Green, R. Sicree, and H. King, “Global prevalence of diabetes: estimates for the year 2000 and projections for 2030,” Diabetes Care 27(5), 1047–1053 (2004). [CrossRef] [PubMed]

]. This high blood sugar will often cause symptoms of frequent urination, increased hunger and increased thirst. The two types that affect the general population are known as Type 1 and Type 2 diabetes. Type 1 diabetes is an autoimmune disease with pancreatic islet beta cell destruction. This results in the inability to maintain glucose homoeostasis. Susceptibility to Type 1 largely inherited, but there are also environmental triggers that are not fully understood. Of those with Type 1 diabetes, 50-60% of patients are under 18 years of age [3

3. D. Daneman, “Type 1 diabetes,” Lancet 367(9513), 847–858 (2006). [CrossRef] [PubMed]

]. Type 2 diabetes is characterized by insulin resistance and deficient beta cell function [4

4. M. Stumvoll, B. J. Goldstein, and T. W. van Haeften, “Type 2 diabetes: principles of pathogenesis and therapy,” Lancet 365(9467), 1333–1346 (2005). [CrossRef] [PubMed]

]. Both Type 1 and Type 2 diabetes are chronic conditions that usually cannot be cured easily. The present method by which many diabetics control their blood glucose levels is by a finger prick several times a day to obtain a drop of blood, in which the glucose is then determined by an analytical chemical reaction. This invasive procedure limits the frequency of monitoring and so may give inadequate control of the long-term complications of the disease. Thus, a continuous, noninvasive method for monitoring body glucose levels would be of great advantage [5

5. J. Y. Qu and B. C. Wilson, “Monte Carlo modeling studies of the effect of physiological factors andother analytes on the determination of glucose concentration in vivoby near infrared optical absorption and scattering measurements,” J. Biomed. Opt. 2(3), 319–325 (1997). [CrossRef] [PubMed]

].

Various optical methods proposed for noninvasive glucose monitoring include absorption and scattering of light in the near-IR spectral range [6

6. J. T. Olesberg, L. Liu, V. Van Zee, and M. A. Arnold, “In vivo near-infrared spectroscopy of rat skin tissue with varying blood glucose levels,” Anal. Chem. 78(1), 215–223 (2006). [CrossRef] [PubMed]

], Raman spectroscopy [7

7. A. M. Enejder, T. G. Scecina, J. Oh, M. Hunter, W. C. Shih, S. Sasic, G. L. Horowitz, and M. S. Feld, “Raman spectroscopy for noninvasive glucose measurements,” J. Biomed. Opt. 10(3), 031114 (2005). [CrossRef] [PubMed]

10

10. N. C. Dingari, I. Barman, J. W. Kang, C. R. Kong, R. R. Dasari, and M. S. Feld, “Wavelength selection-based nonlinear calibration for transcutaneous blood glucose sensing using Raman spectroscopy,” J. Biomed. Opt. 16(8), 087009 (2011). [CrossRef] [PubMed]

], polarimetry [11

11. Q. Wan, G. L. Coté, and J. B. Dixon, “Dual-wavelength polarimetry for monitoring glucose in the presence of varying birefringence,” J. Biomed. Opt. 10(2), 024029 (2005). [CrossRef] [PubMed]

13

13. G. Purvinis, B. D. Cameron, and D. M. Altrogge, “Noninvasive polarimetric-based glucose monitoring: an in vivo study,” J. Diabetes Sci. Tech. 5(2), 380–387 (2011). [CrossRef] [PubMed]

], photoacoustics [14

14. R. Weiss, Y. Yegorchikov, A. Shusterman, and I. Raz, “Noninvasive continuous glucose monitoring using photoacoustic technology-results from the first 62 subjects,” Diabetes Technol. Ther. 9(1), 68–74 (2007). [CrossRef] [PubMed]

, 15

15. H. A. MacKenzie, H. S. Ashton, S. Spiers, Y. Shen, S. S. Freeborn, J. Hannigan, J. Lindberg, and P. Rae, “Advances in photoacoustic noninvasive glucose testing,” Clin. Chem. 45(9), 1587–1595 (1999). [PubMed]

] and time-of-flight spectroscopy [16

16. A. P. Popov, A. V. Priezzhev, and R. Myllylä, “Glucose content monitoring with time-of-flight technique in aqueous Intralipid solution imitating human skin: Monte Carlo simulation,” Proc. SPIE 5862, 586214 (2005). [CrossRef]

, 17

17. M. Kinnunen, A. P. Popov, J. Plucinski, R. A. Myllyla, and A. V. Priezzhev, “Measurements of glucose content in scattering media with time-of-flight technique; comparison with Monte Carlo simulations,” Proc. SPIE 5474, 181–191 (2004). [CrossRef]

]. Although these techniques are promising, they require further development to provide clinically acceptable accuracy, specificity, and reproducibility. Optical coherence tomography (OCT) was proposed previously for blood glucose monitoring and was tested in vivo [18

18. R. O. Esenaliev, K. V. Larin, I. V. Larina, and M. Motamedi, “Noninvasive monitoring of glucose concentration with optical coherence tomography,” Opt. Lett. 26(13), 992–994 (2001). [CrossRef] [PubMed]

22

22. R. Y. He, H. J. Wei, H. M. Gu, Z. G. Zhu, Y. Q. Zhang, X. Guo, and T. Cai, “Effects of optical clearing agents on noninvasive blood glucose monitoring with optical coherence tomo graphy: a pilot study,” J. Biomed. Opt. 17(10), 101513 (2012).

] and in vitro [23

23. K. V. Larin, T. Akkin, R. Esenaliev, M. Motamedi, and M. Milner, “Phase-sensitive optical low-coherence reflectometry for the detection of analyte concentration,” Appl. Opt. 43(17), 3408–3414 (2004).

, 24

24. M. Kinnunen, R. Myllylä, T. Jokela, and S. Vainio, “In vitro studies toward noninvasive glucose monitoring with optical coherence tomography,” Appl. Opt. 45(10), 2251–2260 (2006). [CrossRef] [PubMed]

]. It was shown that OCT is capable of detecting changes in blood glucose concentration as small as a clinically acceptable value of 20 mg/dl [19

19. R. Kuranov, D. Prough, V. Sapozhnikova, I. Cicenaite, and R. Esenaliev, “In vivo application of 2-D lateral scanning mode optical coherence tomography for glucose sensing,” Proc. SPIE 6007, 90–95 (2005). [CrossRef]

, 20

20. R. V. Kuranov, V. V. Sapozhnikova, D. S. Prough, I. Cicenaite, and R. O. Esenaliev, “In vivo study of glucose-induced changes in skin properties assessed with optical coherence tomography,” Phys. Med. Biol. 51(16), 3885–3900 (2006). [CrossRef] [PubMed]

]. The optical properties themselves can potentially provide information to monitor tissue metabolic status or to diagnose diseases. In particular, some possible approaches are based on the effect of glucose on light transport in tissue. It has been reported that glucose also changes the optical scattering properties of tissues [5

5. J. Y. Qu and B. C. Wilson, “Monte Carlo modeling studies of the effect of physiological factors andother analytes on the determination of glucose concentration in vivoby near infrared optical absorption and scattering measurements,” J. Biomed. Opt. 2(3), 319–325 (1997). [CrossRef] [PubMed]

, 25

25. J. S. Maier, S. A. Walker, S. Fantini, M. A. Franceschini, and E. Gratton, “Possible correlation between blood glucose concentration and the reduced scattering coefficient of tissues in the near infrared,” Opt. Lett. 19(24), 2062–2064 (1994). [CrossRef] [PubMed]

31

31. X. D. Wang, G. Yao, and L. V. Wang, “Monte Carlo model and single-scattering approximation of the propagation of polarized light in turbid media containing glucose,” Appl. Opt. 41(4), 792–801 (2002). [CrossRef] [PubMed]

]. Light scattering occurs in tissues because of the mismatch of refractive index between the extracellular fluid (ECF) and the membranes of the cells composing the tissue. In the near-infrared region (NIR), the refractive index of the ECF is nECF≈1.348-1.352, while the refractive index of the cellular membranes and protein aggregates is in the range ncell≈1.350-1.460 [29

29. R. Poddar, S. R. Sharma, J. Andrews, and P. Sen, “Correlation between glucose concentration and reduced scattering coefficients in turbid media using optical coherence tomography,” Curr. Sci. 95(3), 340–344 (2008).

]. It is well known that adding sugar to water increases the refractive index of the solution. Similarly, adding glucose to blood in turn raises the refractive index of the ECF, which will cause a change in the scattering characteristics of the tissue as a whole. Hence, tissue glucose levels are correlated with scattering coefficients based on changes in the refractive index of the ECF. Of late, measurements on light scattering by blood show promising correlation between blood glucose and reduced scattering coefficient [25

25. J. S. Maier, S. A. Walker, S. Fantini, M. A. Franceschini, and E. Gratton, “Possible correlation between blood glucose concentration and the reduced scattering coefficient of tissues in the near infrared,” Opt. Lett. 19(24), 2062–2064 (1994). [CrossRef] [PubMed]

]. At the same time, monitoring of glycemic status in patients with diabetes requires determination of blood glucose concentration. Significant efforts have been made by several groups in the past few decades to develop a biosensor for noninvasive blood glucose analysis.

In this paper, the objectives of our study were to continuously monitor the alterations of in vivo human skin optical properties during oral intake of different sugars with a spectral domain OCT (SD-OCT) system, and we also quantitatively analyzed the different effects of sucrose, fructose and glucose on human skin optical properties, respectively.

2. Methods and materials

2.1 OCT system

The experiments were performed with a SD-OCT system. It is made by Shenzhen MOPTIM Imaging Technique Co., Ltd., China. A schematic of the OCT system was shown in the literature [40

40. H. J. Wei, G. Wu, Z. Guo, H. Yang, Y. He, S. Xie, and X. Guo, “Assessment of the effects of ultrasound-mediated glucose on permeability of normal, benign, and cancerous human lung tissues with the Fourier-domain optical coherence tomography,” J. Biomed. Opt. 17(11), 116006 (2012). [CrossRef] [PubMed]

]. The optical source used in this system is a low-coherence broadband super luminescent diode with a wavelength of 830 ± 40 nm and an output power of 5 mW. The SD-OCT system provides an axial resolution of 12 μm and a transverse resolution of 15 μm in free space, determined by the focal spot size of the probe beam. The signal-to-noise ratio of the OCT system is measured to be 120 dB. Two-dimensional (2-D) images are obtained by scanning the incident beam over the sample surface in the lateral direction and in-depth (A-scan) scanning by the interferometer. The acquisition time per OCT image is about 180 ms, corresponding to an A-scan frequency of 2000 Hz. A computer is used to control the OCT system with a data acquisition software written in Lab View 7.2-D. OCT images obtained in the experiment were stored in the computer for further processing.

2.2 Materials and measurements

In this study, 32 healthy volunteers were randomly divided into four groups, A, B, C and D, and 8 subjects in each group. Each volunteer was asked to orally administer 50 g glucose dissolving in 400 ml water in group B. In group C, each volunteer was asked to orally administer 50 g sucrose dissolving in 400 ml water. In group D, each volunteer was asked to orally administer 50 g fructose dissolving in 400 ml water. Group A, acts as a control group, orally administer equivalent volume of water only. The experiments were performed in all volunteers starting at 8: 00 A.M. (time = 0) after an overnight fast. Each subject was asked to drink the solution in two minutes. During the measurement, each volunteer was asked to remain still to minimize motion artifacts, and no food and drinks were permitted. BGC was monitored serially at about 10 min intervals using one touch ultra easy glucose analyzers. Whole-blood samples were drawn from the subject’s fingertips. The duration of each individual experiment was about 160 min. Before oral administration of sugars, the detected regions of subjects were measured for about 10 min to establish a baseline by OCT. Then continuous monitoring of tissue optical properties was performed for up to 150 min. Each volunteer has completed the experiments. The room temperature was maintained at 20°C throughout the experiment, so as to eliminate influences caused by temperature fluctuation [46

46. K. V. Larin, M. Motamedi, T. V. Ashitkov, and R. O. Esenaliev, “Specificity of noninvasive blood glucose sensing using optical coherence tomography technique: a pilot study,” Phys. Med. Biol. 48(10), 1371–1390 (2003). [CrossRef] [PubMed]

].

2.3 Methods

In order to characterize the changes of skin optical properties during oral intake of different sugars, the attenuation coefficients of each group were calculated from the 2-D OCT image, as it carries the information of the reflected light intensity distribution in depth of the tissue. The reflected light intensity depends on the tissue optical properties. For media with absorption as described by the single scattering approximation, the light travels in a ballistic way, and Beer’s law can be applied to calculate the total OCT attenuation coefficient: μt = μa + μs. These are physical properties unique to the biological tissue, which play a vital role in the assessment of the tissue feature [37

37. Y. Yang, T. Wang, N. C. Biswal, X. Wang, M. Sanders, M. Brewer, and Q. Zhu, “Optical scattering coefficient estimated by optical coherence tomography correlates with collagen content in ovarian tissue,” J. Biomed. Opt. 16(9), 090504 (2011). [CrossRef] [PubMed]

, 47

47. V. M. Kodach, D. J. Faber, J. van Marle, T. G. van Leeuwen, and J. Kalkman, “Determination of the scattering anisotropy with optical coherence tomography,” Opt. Express 19(7), 6131–6140 (2011). [CrossRef] [PubMed]

, 48

48. D. Levitz, L. Thrane, M. Frosz, P. Andersen, C. Andersen, S. Andersson-Engels, J. Valanciunaite, J. Swartling, and P. Hansen, “Determination of optical scattering properties of highly-scattering media in optical coherence tomography images,” Opt. Express 12(2), 249–259 (2004). [CrossRef] [PubMed]

]. In this current OCT system case, the measured signal is defined as [37

37. Y. Yang, T. Wang, N. C. Biswal, X. Wang, M. Sanders, M. Brewer, and Q. Zhu, “Optical scattering coefficient estimated by optical coherence tomography correlates with collagen content in ovarian tissue,” J. Biomed. Opt. 16(9), 090504 (2011). [CrossRef] [PubMed]

, 47

47. V. M. Kodach, D. J. Faber, J. van Marle, T. G. van Leeuwen, and J. Kalkman, “Determination of the scattering anisotropy with optical coherence tomography,” Opt. Express 19(7), 6131–6140 (2011). [CrossRef] [PubMed]

49

49. L. Thrane, H. T. Yura, and P. E. Andersen, “Analysis of optical coherence tomography systems based on the extended Huygens-Fresenel principle,” J. Opt. Soc. Am. A 17(3), 484–490 (2000). [CrossRef]

]:
[i2(z)]1/2(i20)1/2[exp(2μtz)]1/2.
(1)
where the i2(z) is the photodetector heterodyne signal current received by an OCT system from the probing depth z and the mean square heterodyne signal i20. The result of the OCT study is the measurement of optical backscattering or reflectance R(z)[i2(z)]1/2 from a tissue versus axial ranging distance, or depth, z. The reflectance depends on the optical properties of tissue, i.e., the total attenuation coefficient μt. Thus, combined with Eq. (1) and R(z) it follows that the reflected power can be approximately proportional to -μtz in exponential scale according to the single scattering model:
R(z)=I0a(z)exp(μtz).
(2)
Here I0 is the optical power launched into the tissue sample and a(z) is the reflectivity of the tissue sample at the depth of z. Therefore, measurement of OCT reflectance for depths z1 and z2 allows for approximately evaluating the attenuation coefficient and its temporal behavior. This evaluation is due to reduction of the tissue-scattering coefficient caused by increased BGC in the tissue if reflectivity a(z) is considered as weakly dependent on depth for a homogeneous tissue layer. The μt theoretically can be obtained from the reflectance intensity measurements at two different depths, z1 and z2 [37

37. Y. Yang, T. Wang, N. C. Biswal, X. Wang, M. Sanders, M. Brewer, and Q. Zhu, “Optical scattering coefficient estimated by optical coherence tomography correlates with collagen content in ovarian tissue,” J. Biomed. Opt. 16(9), 090504 (2011). [CrossRef] [PubMed]

, 47

47. V. M. Kodach, D. J. Faber, J. van Marle, T. G. van Leeuwen, and J. Kalkman, “Determination of the scattering anisotropy with optical coherence tomography,” Opt. Express 19(7), 6131–6140 (2011). [CrossRef] [PubMed]

49

49. L. Thrane, H. T. Yura, and P. E. Andersen, “Analysis of optical coherence tomography systems based on the extended Huygens-Fresenel principle,” J. Opt. Soc. Am. A 17(3), 484–490 (2000). [CrossRef]

]:
μt=1Δzln[R(z1)R(z2)].
(3)
Where z1 and z2 are two different depths for A-scan (z-axis); Δz=|z1z2|; and R(z1) and R(z2) are magnitudes of reflectance for these scanning depths [50

50. O. Zhernovaya, V. V. Tuchin, and M. J. Leahy, “Blood optical clearing studied by optical coherence tomography,” J. Biomed. Opt. 18(2), 026014 (2013). [CrossRef] [PubMed]

]. Noise is inevitable in the measurement, thus a final result should be obtained using a least-square fitting method in order to improve the accuracy of determining μt value. An averaged intensity profile as a function of depth was obtained by averaging the 2-D images laterally over approximately 1 mm, which was enough for speckle noise suppression. A best-fit exponential curve was applied to the averaged intensity profiles of each group since the noise in the measurement is unavoidable. The data acquisition and processing are shown in Fig. 1
Fig. 1 Example of an OCT image of skin tissue and the averaged OCT signal profile versus depth extracted from the selected region in OCT image.
. In this study, the depth interval of 500–600 µm from the skin surface was chosen to calculate the attenuation coefficient by Eq. (3). Because near the dermis–hypodermis junction at 500–600 µm where the most prominent glucose-induced changes were found [20

20. R. V. Kuranov, V. V. Sapozhnikova, D. S. Prough, I. Cicenaite, and R. O. Esenaliev, “In vivo study of glucose-induced changes in skin properties assessed with optical coherence tomography,” Phys. Med. Biol. 51(16), 3885–3900 (2006). [CrossRef] [PubMed]

].

2.4 Statistical analysis

The data from all samples were presented as means ± SD and analyzed by an SPSS 16.0 software paired-test. The p <0.05 value indicated significant difference.

3. Results and discussion

Figure 2
Fig. 2 OCT signal intensity versus depth profiles recorded from the human skin in vivo after oral intake of glucose solution (group B), sucrose solution (group C), fructose solution (group D) and water (control group, group A) at 50 min, respectively.
shows the OCT signal intensity versus depth profiles recorded from human skin in vivo after oral intake of glucose solution (group B), sucrose solution (group C), fructose solution (group D) and water (control group, group A) at 50 min, respectively. We can see from Fig. 2 that OCT signal intensity in human skin after drinking sucrose solution (group C) is markedly higher than that after drinking water (group A) in the depth range of about 450 to 1200 um at 50 min, and OCT signal intensity after drinking glucose solution (group B) is higher than that after drinking sucrose solution (group C) in the depth range of about 450 to 1200 um at 50 min. However the OCT signal intensity in human skin after drinking fructose solution (group D) is nearly the same with that after drinking water (group A) at 50 min, and there is merely small difference in the OCT signal intensity between group D and group A in the depth range of 800 to 1200 um at the same time. These differences in the OCT signal intensity in human skin between control group and experimental group were mainly caused by the differences in blood glucose concentration after oral different sugars intake.

4. Conclusion

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant Nos. 60778047 and 61275187), Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20114407110001), the Natural Science Foundation of Guangdong Province of China (Grant Nos. 06025080 and 9251063101000009), the Key Science and Technology Project of Guangdong Province of China (Grant Nos. 2005B50101015 and 2008B090500125), the Key Science and Technology Project of Guangzhou City of China (Grant No. 2008Z1-D391), and Key Laboratory of Optoelectronic Science and Technology for Medicine (Fujian Normal University), Ministry of Education, China (Grant No. JYG1202)

References and links

1.

X. X. Guo, A. Mandelis, and B. Zinman, “Noninvasive glucose detection in human skin using wavelength modulated differential laser photothermal radiometry,” Biomed. Opt. Express 3(11), 3012–3021 (2012). [CrossRef] [PubMed]

2.

S. Wild, G. Roglic, A. Green, R. Sicree, and H. King, “Global prevalence of diabetes: estimates for the year 2000 and projections for 2030,” Diabetes Care 27(5), 1047–1053 (2004). [CrossRef] [PubMed]

3.

D. Daneman, “Type 1 diabetes,” Lancet 367(9513), 847–858 (2006). [CrossRef] [PubMed]

4.

M. Stumvoll, B. J. Goldstein, and T. W. van Haeften, “Type 2 diabetes: principles of pathogenesis and therapy,” Lancet 365(9467), 1333–1346 (2005). [CrossRef] [PubMed]

5.

J. Y. Qu and B. C. Wilson, “Monte Carlo modeling studies of the effect of physiological factors andother analytes on the determination of glucose concentration in vivoby near infrared optical absorption and scattering measurements,” J. Biomed. Opt. 2(3), 319–325 (1997). [CrossRef] [PubMed]

6.

J. T. Olesberg, L. Liu, V. Van Zee, and M. A. Arnold, “In vivo near-infrared spectroscopy of rat skin tissue with varying blood glucose levels,” Anal. Chem. 78(1), 215–223 (2006). [CrossRef] [PubMed]

7.

A. M. Enejder, T. G. Scecina, J. Oh, M. Hunter, W. C. Shih, S. Sasic, G. L. Horowitz, and M. S. Feld, “Raman spectroscopy for noninvasive glucose measurements,” J. Biomed. Opt. 10(3), 031114 (2005). [CrossRef] [PubMed]

8.

J. M. Yuen, N. C. Shah, J. T. Walsh Jr, M. R. Glucksberg, and R. P. Van Duyne, “Transcutaneous glucose sensing by surface-enhanced spatially offset Raman spectroscopy in a rat model,” Anal. Chem. 82(20), 8382–8385 (2010). [CrossRef] [PubMed]

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N. C. Dingari, I. Barman, G. P. Singh, J. W. Kang, R. R. Dasari, and M. S. Feld, “Investigation of the specificity of Raman spectroscopy in non-invasive blood glucose measurements,” Anal. Bioanal. Chem. 400(9), 2871–2880 (2011). [CrossRef] [PubMed]

10.

N. C. Dingari, I. Barman, J. W. Kang, C. R. Kong, R. R. Dasari, and M. S. Feld, “Wavelength selection-based nonlinear calibration for transcutaneous blood glucose sensing using Raman spectroscopy,” J. Biomed. Opt. 16(8), 087009 (2011). [CrossRef] [PubMed]

11.

Q. Wan, G. L. Coté, and J. B. Dixon, “Dual-wavelength polarimetry for monitoring glucose in the presence of varying birefringence,” J. Biomed. Opt. 10(2), 024029 (2005). [CrossRef] [PubMed]

12.

B. D. Cameron and Y. F. Li, “Polarization-based diffuse reflectance imaging for noninvasive measurement of glucose,” J. Diabetes Sci. Tech. 1(6), 873–878 (2007). [CrossRef] [PubMed]

13.

G. Purvinis, B. D. Cameron, and D. M. Altrogge, “Noninvasive polarimetric-based glucose monitoring: an in vivo study,” J. Diabetes Sci. Tech. 5(2), 380–387 (2011). [CrossRef] [PubMed]

14.

R. Weiss, Y. Yegorchikov, A. Shusterman, and I. Raz, “Noninvasive continuous glucose monitoring using photoacoustic technology-results from the first 62 subjects,” Diabetes Technol. Ther. 9(1), 68–74 (2007). [CrossRef] [PubMed]

15.

H. A. MacKenzie, H. S. Ashton, S. Spiers, Y. Shen, S. S. Freeborn, J. Hannigan, J. Lindberg, and P. Rae, “Advances in photoacoustic noninvasive glucose testing,” Clin. Chem. 45(9), 1587–1595 (1999). [PubMed]

16.

A. P. Popov, A. V. Priezzhev, and R. Myllylä, “Glucose content monitoring with time-of-flight technique in aqueous Intralipid solution imitating human skin: Monte Carlo simulation,” Proc. SPIE 5862, 586214 (2005). [CrossRef]

17.

M. Kinnunen, A. P. Popov, J. Plucinski, R. A. Myllyla, and A. V. Priezzhev, “Measurements of glucose content in scattering media with time-of-flight technique; comparison with Monte Carlo simulations,” Proc. SPIE 5474, 181–191 (2004). [CrossRef]

18.

R. O. Esenaliev, K. V. Larin, I. V. Larina, and M. Motamedi, “Noninvasive monitoring of glucose concentration with optical coherence tomography,” Opt. Lett. 26(13), 992–994 (2001). [CrossRef] [PubMed]

19.

R. Kuranov, D. Prough, V. Sapozhnikova, I. Cicenaite, and R. Esenaliev, “In vivo application of 2-D lateral scanning mode optical coherence tomography for glucose sensing,” Proc. SPIE 6007, 90–95 (2005). [CrossRef]

20.

R. V. Kuranov, V. V. Sapozhnikova, D. S. Prough, I. Cicenaite, and R. O. Esenaliev, “In vivo study of glucose-induced changes in skin properties assessed with optical coherence tomography,” Phys. Med. Biol. 51(16), 3885–3900 (2006). [CrossRef] [PubMed]

21.

K. V. Larin, M. S. Eledrisi, M. Motamedi, and R. O. Esenaliev, “Noninvasive blood glucose monitoring with optical coherence tomography: A pilot study in human subjects,” Diabetes Care 25(12), 2263–2267 (2002). [CrossRef] [PubMed]

22.

R. Y. He, H. J. Wei, H. M. Gu, Z. G. Zhu, Y. Q. Zhang, X. Guo, and T. Cai, “Effects of optical clearing agents on noninvasive blood glucose monitoring with optical coherence tomo graphy: a pilot study,” J. Biomed. Opt. 17(10), 101513 (2012).

23.

K. V. Larin, T. Akkin, R. Esenaliev, M. Motamedi, and M. Milner, “Phase-sensitive optical low-coherence reflectometry for the detection of analyte concentration,” Appl. Opt. 43(17), 3408–3414 (2004).

24.

M. Kinnunen, R. Myllylä, T. Jokela, and S. Vainio, “In vitro studies toward noninvasive glucose monitoring with optical coherence tomography,” Appl. Opt. 45(10), 2251–2260 (2006). [CrossRef] [PubMed]

25.

J. S. Maier, S. A. Walker, S. Fantini, M. A. Franceschini, and E. Gratton, “Possible correlation between blood glucose concentration and the reduced scattering coefficient of tissues in the near infrared,” Opt. Lett. 19(24), 2062–2064 (1994). [CrossRef] [PubMed]

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L. Heinemann, U. Krämer, H.-M. Klötzer, M. Hein, D. Volz, M. Hermann, T. Heise, and K. Rave, “Noninvasive glucose measurement by monitoring of scattering coefficient during oral glucose tolerance tests,” Diabetes Technol. Ther. 2(2), 211–220 (2000). [CrossRef] [PubMed]

29.

R. Poddar, S. R. Sharma, J. Andrews, and P. Sen, “Correlation between glucose concentration and reduced scattering coefficients in turbid media using optical coherence tomography,” Curr. Sci. 95(3), 340–344 (2008).

30.

M. Kohl, M. Essenpreis, and M. Cope, “The influence of glucose concentration upon the transport of light in tissue-simulating phantoms,” Phys. Med. Biol. 40(7), 1267–1287 (1995). [CrossRef] [PubMed]

31.

X. D. Wang, G. Yao, and L. V. Wang, “Monte Carlo model and single-scattering approximation of the propagation of polarized light in turbid media containing glucose,” Appl. Opt. 41(4), 792–801 (2002). [CrossRef] [PubMed]

32.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and et, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991). [CrossRef] [PubMed]

33.

J. M. Schmitt, A. Knüttel, and R. F. Bonner, “Measurement of optical properties of biological tissues by low-coherence reflectometry,” Appl. Opt. 32(30), 6032–6042 (1993). [CrossRef] [PubMed]

34.

A. I. Kholodnykh, I. Y. Petrova, K. V. Larin, M. Motamedi, and R. O. Esenaliev, “Precision of measurement of tissue optical properties with optical coherence tomography,” Appl. Opt. 42(16), 3027–3037 (2003). [CrossRef] [PubMed]

35.

P. Lee, W. R. Gao, and X. L. Zhang, “Performance of single-scattering model versus multiple-scattering model in the determination of optical properties of biological tissue with optical coherence tomography,” Appl. Opt. 49(18), 3538–3544 (2010). [CrossRef] [PubMed]

36.

D. J. Faber, F. J. van der Meer, M. C. G. Aalders, and T. van Leeuwen, “Quantitative measurement of attenuation coefficients of weakly scattering media using optical coherence tomography,” Opt. Express 12(19), 4353–4365 (2004). [CrossRef] [PubMed]

37.

Y. Yang, T. Wang, N. C. Biswal, X. Wang, M. Sanders, M. Brewer, and Q. Zhu, “Optical scattering coefficient estimated by optical coherence tomography correlates with collagen content in ovarian tissue,” J. Biomed. Opt. 16(9), 090504 (2011). [CrossRef] [PubMed]

38.

Y. Yang, T. Wang, X. Wang, M. Sanders, M. Brewer, and Q. Zhu, “Quantitative analysis of estimated scattering coefficient and phase retardation for ovarian tissue characterization,” Biomed. Opt. Express 3(7), 1548–1556 (2012). [CrossRef] [PubMed]

39.

Y. Yang, T. Wang, M. Brewer, and Q. Zhu, “Quantitative analysis of angle-resolved scattering properties of ovarian tissue using optical coherence tomography,” J. Biomed. Opt. 17(9), 090530 (2012). [CrossRef] [PubMed]

40.

H. J. Wei, G. Wu, Z. Guo, H. Yang, Y. He, S. Xie, and X. Guo, “Assessment of the effects of ultrasound-mediated glucose on permeability of normal, benign, and cancerous human lung tissues with the Fourier-domain optical coherence tomography,” J. Biomed. Opt. 17(11), 116006 (2012). [CrossRef] [PubMed]

41.

R. V. Kuranov, V. V. Sapozhnikova, D. S. Prough, I. Cicenaite, and R. O. Esenaliev, “Prediction capability of optical coherence tomography for blood glucose concentration monitoring,” J. Diabetes Sci. Tech. 1(4), 470–477 (2007). [CrossRef]

42.

D. A. Southgate, “Digestion and metabolism of sugars,” Am. J. Clin. Nutr. 62(1Suppl), 203S–210S(1995). [PubMed]

43.

P. A. Mayes, “Intermediary metabolism of fructose,” Am. J. Clin. Nutr. 58(5Suppl), 754S–765S (1993). [PubMed]

44.

F. Q. Nuttal, M. A. Khan, and M. C. Gannon, “Peripheral glucose appearance rate following fructose ingestion in normal subjects,” Metabolism 49(12), 1565–1571 (2000). [CrossRef] [PubMed]

45.

J. P. Bantle, D. C. Laine, G. W. Castle, J. W. Thomas, B. J. Hoogwerf, and F. C. Goetz, “Postprandial glucose and insulin responses to meals containing different carbohydrates in normal and diabetic subjects,” N. Engl. J. Med. 309(1), 7–12 (1983). [CrossRef] [PubMed]

46.

K. V. Larin, M. Motamedi, T. V. Ashitkov, and R. O. Esenaliev, “Specificity of noninvasive blood glucose sensing using optical coherence tomography technique: a pilot study,” Phys. Med. Biol. 48(10), 1371–1390 (2003). [CrossRef] [PubMed]

47.

V. M. Kodach, D. J. Faber, J. van Marle, T. G. van Leeuwen, and J. Kalkman, “Determination of the scattering anisotropy with optical coherence tomography,” Opt. Express 19(7), 6131–6140 (2011). [CrossRef] [PubMed]

48.

D. Levitz, L. Thrane, M. Frosz, P. Andersen, C. Andersen, S. Andersson-Engels, J. Valanciunaite, J. Swartling, and P. Hansen, “Determination of optical scattering properties of highly-scattering media in optical coherence tomography images,” Opt. Express 12(2), 249–259 (2004). [CrossRef] [PubMed]

49.

L. Thrane, H. T. Yura, and P. E. Andersen, “Analysis of optical coherence tomography systems based on the extended Huygens-Fresenel principle,” J. Opt. Soc. Am. A 17(3), 484–490 (2000). [CrossRef]

50.

O. Zhernovaya, V. V. Tuchin, and M. J. Leahy, “Blood optical clearing studied by optical coherence tomography,” J. Biomed. Opt. 18(2), 026014 (2013). [CrossRef] [PubMed]

51.

J. V. Bjørnholt, G. Erikssen, E. Aaser, L. Sandvik, S. Nitter-Hauge, J. Jervell, J. Erikssen, and E. Thaulow, “Fasting blood glucose: an underestimated risk factor for cardiovascular death. Results from a 22-year follow-up of healthy nondiabetic men,” Diabetes Care 22(1), 45–49 (1999). [CrossRef] [PubMed]

52.

G. McGarraugh, D. Price, S. Schwartz, and R. Weinstein, “Physiological influences on off-finger glucose testing,” Diabetes Technol. Ther. 3(3), 367–376 (2001). [CrossRef] [PubMed]

53.

G. M. Steil, K. Rebrin, J. Mastrototaro, B. Bernaba, and M. F. Saad, “Determination of plasma glucose during rapid glucose excursions with a subcutaneous glucose sensor,” Diabetes Technol. Ther. 5(1), 27–31 (2003). [CrossRef] [PubMed]

54.

A. J. M. Schoonen and K. J. C. Wientjes, “A model for transport of glucose in adipose tissue to a microdialysis probe,” Diabetes Technol. Ther. 5(4), 589–598 (2003). [CrossRef] [PubMed]

55.

K. Jungheim and T. Koschinsky, “Glucose Monitoring at the Arm: Risky delays of hypoglycemia and hyperglycemia detection,” Diabetes Care 25(6), 956–960 (2002). [CrossRef] [PubMed]

OCIS Codes
(100.2960) Image processing : Image analysis
(110.4500) Imaging systems : Optical coherence tomography
(170.3880) Medical optics and biotechnology : Medical and biological imaging

ToC Category:
Noninvasive Optical Diagnostics

History
Original Manuscript: December 3, 2013
Revised Manuscript: January 14, 2014
Manuscript Accepted: February 12, 2014
Published: February 28, 2014

Citation
Yuqing Zhang, Guoyong Wu, Huajiang Wei, Zhouyi Guo, Hongqin Yang, Yonghong He, Shusen Xie, and Ying Liu, "Continuous noninvasive monitoring of changes in human skin optical properties during oral intake of different sugars with optical coherence tomography," Biomed. Opt. Express 5, 990-999 (2014)
http://www.opticsinfobase.org/boe/abstract.cfm?URI=boe-5-4-990


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References

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  28. L. Heinemann, U. Krämer, H.-M. Klötzer, M. Hein, D. Volz, M. Hermann, T. Heise, and K. Rave, “Noninvasive glucose measurement by monitoring of scattering coefficient during oral glucose tolerance tests,” Diabetes Technol. Ther.2(2), 211–220 (2000). [CrossRef] [PubMed]
  29. R. Poddar, S. R. Sharma, J. Andrews, and P. Sen, “Correlation between glucose concentration and reduced scattering coefficients in turbid media using optical coherence tomography,” Curr. Sci.95(3), 340–344 (2008).
  30. M. Kohl, M. Essenpreis, and M. Cope, “The influence of glucose concentration upon the transport of light in tissue-simulating phantoms,” Phys. Med. Biol.40(7), 1267–1287 (1995). [CrossRef] [PubMed]
  31. X. D. Wang, G. Yao, and L. V. Wang, “Monte Carlo model and single-scattering approximation of the propagation of polarized light in turbid media containing glucose,” Appl. Opt.41(4), 792–801 (2002). [CrossRef] [PubMed]
  32. D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and et, “Optical coherence tomography,” Science254(5035), 1178–1181 (1991). [CrossRef] [PubMed]
  33. J. M. Schmitt, A. Knüttel, and R. F. Bonner, “Measurement of optical properties of biological tissues by low-coherence reflectometry,” Appl. Opt.32(30), 6032–6042 (1993). [CrossRef] [PubMed]
  34. A. I. Kholodnykh, I. Y. Petrova, K. V. Larin, M. Motamedi, and R. O. Esenaliev, “Precision of measurement of tissue optical properties with optical coherence tomography,” Appl. Opt.42(16), 3027–3037 (2003). [CrossRef] [PubMed]
  35. P. Lee, W. R. Gao, and X. L. Zhang, “Performance of single-scattering model versus multiple-scattering model in the determination of optical properties of biological tissue with optical coherence tomography,” Appl. Opt.49(18), 3538–3544 (2010). [CrossRef] [PubMed]
  36. D. J. Faber, F. J. van der Meer, M. C. G. Aalders, and T. van Leeuwen, “Quantitative measurement of attenuation coefficients of weakly scattering media using optical coherence tomography,” Opt. Express12(19), 4353–4365 (2004). [CrossRef] [PubMed]
  37. Y. Yang, T. Wang, N. C. Biswal, X. Wang, M. Sanders, M. Brewer, and Q. Zhu, “Optical scattering coefficient estimated by optical coherence tomography correlates with collagen content in ovarian tissue,” J. Biomed. Opt.16(9), 090504 (2011). [CrossRef] [PubMed]
  38. Y. Yang, T. Wang, X. Wang, M. Sanders, M. Brewer, and Q. Zhu, “Quantitative analysis of estimated scattering coefficient and phase retardation for ovarian tissue characterization,” Biomed. Opt. Express3(7), 1548–1556 (2012). [CrossRef] [PubMed]
  39. Y. Yang, T. Wang, M. Brewer, and Q. Zhu, “Quantitative analysis of angle-resolved scattering properties of ovarian tissue using optical coherence tomography,” J. Biomed. Opt.17(9), 090530 (2012). [CrossRef] [PubMed]
  40. H. J. Wei, G. Wu, Z. Guo, H. Yang, Y. He, S. Xie, and X. Guo, “Assessment of the effects of ultrasound-mediated glucose on permeability of normal, benign, and cancerous human lung tissues with the Fourier-domain optical coherence tomography,” J. Biomed. Opt.17(11), 116006 (2012). [CrossRef] [PubMed]
  41. R. V. Kuranov, V. V. Sapozhnikova, D. S. Prough, I. Cicenaite, and R. O. Esenaliev, “Prediction capability of optical coherence tomography for blood glucose concentration monitoring,” J. Diabetes Sci. Tech.1(4), 470–477 (2007). [CrossRef]
  42. D. A. Southgate, “Digestion and metabolism of sugars,” Am. J. Clin. Nutr.62(1Suppl), 203S–210S(1995). [PubMed]
  43. P. A. Mayes, “Intermediary metabolism of fructose,” Am. J. Clin. Nutr.58(5Suppl), 754S–765S (1993). [PubMed]
  44. F. Q. Nuttal, M. A. Khan, and M. C. Gannon, “Peripheral glucose appearance rate following fructose ingestion in normal subjects,” Metabolism49(12), 1565–1571 (2000). [CrossRef] [PubMed]
  45. J. P. Bantle, D. C. Laine, G. W. Castle, J. W. Thomas, B. J. Hoogwerf, and F. C. Goetz, “Postprandial glucose and insulin responses to meals containing different carbohydrates in normal and diabetic subjects,” N. Engl. J. Med.309(1), 7–12 (1983). [CrossRef] [PubMed]
  46. K. V. Larin, M. Motamedi, T. V. Ashitkov, and R. O. Esenaliev, “Specificity of noninvasive blood glucose sensing using optical coherence tomography technique: a pilot study,” Phys. Med. Biol.48(10), 1371–1390 (2003). [CrossRef] [PubMed]
  47. V. M. Kodach, D. J. Faber, J. van Marle, T. G. van Leeuwen, and J. Kalkman, “Determination of the scattering anisotropy with optical coherence tomography,” Opt. Express19(7), 6131–6140 (2011). [CrossRef] [PubMed]
  48. D. Levitz, L. Thrane, M. Frosz, P. Andersen, C. Andersen, S. Andersson-Engels, J. Valanciunaite, J. Swartling, and P. Hansen, “Determination of optical scattering properties of highly-scattering media in optical coherence tomography images,” Opt. Express12(2), 249–259 (2004). [CrossRef] [PubMed]
  49. L. Thrane, H. T. Yura, and P. E. Andersen, “Analysis of optical coherence tomography systems based on the extended Huygens-Fresenel principle,” J. Opt. Soc. Am. A17(3), 484–490 (2000). [CrossRef]
  50. O. Zhernovaya, V. V. Tuchin, and M. J. Leahy, “Blood optical clearing studied by optical coherence tomography,” J. Biomed. Opt.18(2), 026014 (2013). [CrossRef] [PubMed]
  51. J. V. Bjørnholt, G. Erikssen, E. Aaser, L. Sandvik, S. Nitter-Hauge, J. Jervell, J. Erikssen, and E. Thaulow, “Fasting blood glucose: an underestimated risk factor for cardiovascular death. Results from a 22-year follow-up of healthy nondiabetic men,” Diabetes Care22(1), 45–49 (1999). [CrossRef] [PubMed]
  52. G. McGarraugh, D. Price, S. Schwartz, and R. Weinstein, “Physiological influences on off-finger glucose testing,” Diabetes Technol. Ther.3(3), 367–376 (2001). [CrossRef] [PubMed]
  53. G. M. Steil, K. Rebrin, J. Mastrototaro, B. Bernaba, and M. F. Saad, “Determination of plasma glucose during rapid glucose excursions with a subcutaneous glucose sensor,” Diabetes Technol. Ther.5(1), 27–31 (2003). [CrossRef] [PubMed]
  54. A. J. M. Schoonen and K. J. C. Wientjes, “A model for transport of glucose in adipose tissue to a microdialysis probe,” Diabetes Technol. Ther.5(4), 589–598 (2003). [CrossRef] [PubMed]
  55. K. Jungheim and T. Koschinsky, “Glucose Monitoring at the Arm: Risky delays of hypoglycemia and hyperglycemia detection,” Diabetes Care25(6), 956–960 (2002). [CrossRef] [PubMed]

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