Study of Methionine Choline Deficient Diet-Induced Steatosis in Mice Using Endogenous Fluorescence Spectroscopy.

Non-alcoholic fatty liver disease is a highly prevalent condition worldwide that increases the risk to develop liver fibrosis, cirrhosis, and hepatocellular carcinoma. Thus, it is imperative to develop novel diagnostic tools that together with liver biopsy help to differentiate mild and advanced degrees of steatosis. Ex-vivo liver samples were collected from mice fed a methionine-choline deficient diet for two or eight weeks, and from a control group. The degree of hepatic steatosis was histologically evaluated, and fat content was assessed by Oil-Red O staining. On the other hand, fluorescence spectroscopy was used for the assessment of the steatosis progression. Fluorescence spectra were recorded at excitation wavelengths of 330, 365, 385, 405, and 415 nm by establishing surface contact of the fiber optic probe with the liver specimens. A multi-variate statistical approach based on principal component analysis followed by quadratic discriminant analysis was applied to spectral data to obtain classifiers able to distinguish mild and moderate stages of steatosis at the different excitation wavelengths. Receiver Operating Characteristic (ROC) curves were computed to compare classifier's performances for each one of the five excitation wavelengths and steatosis stages. Optimal sensitivity and specificity were calculated from the corresponding ROC curves using the Youden index. Intensity in the endogenous fluorescence spectra at the given wavelengths progressively increased according to the time of exposure to diet. The area under the curve of the spectra was able to discriminate control liver samples from those with steatosis and differentiate among the time of exposure to the diet for most of the used excitation wavelengths. High specificities and sensitivities were obtained for every case; however, fluorescence spectra obtained by exciting with 405 nm yielded the best results distinguishing between the mentioned classes with a total classification error of 1.5% and optimal sensitivities and specificities better than 98.6% and 99.3%, respectively.

Combined methods of optical spectroscopy and artificial intelligence in the assessment of experimentally induced non-alcoholic fatty liver.

BACKGROUND AND OBJECTIVE: Due to the existing prevalence of nonalcoholic fatty liver disease (NAFLD) and its relation to the epidemic of obesity in the general population, it is imperative to develop detection and evaluation methods of the early stages of the disease with improved efficacy over the current diagnostic approaches. We aimed to obtain an improved diagnosis, combining methods of optical spectroscopy -diffuse reflectance and fluorescence- with statistical data analysis applied to detect early stages of NAFLD. METHODS: Statistical analysis scheme based on quadratic discriminant analysis followed by canonical discriminant analysis were applied to the diffuse reflectance data combined with endogenous fluorescence spectral data excited at one of these wavelengths: 330, 365, 385, 405 or 415 nm. The statistical scheme was also applied to the combinations of fluorescence spectrum (405 nm) with each one of the other fluorescence spectra. Details of the developed software, including the application of machine learning algorithms to the combination of spectral data followed by classification statistical schemes, are discussed. RESULTS: Steatosis progression was differentiated with little classification error (≤1.3%) by using diffuse reflectance and endogenous fluorescence at different wavelengths. Similar results were obtained using fluorescence at 405 nm and one of the other fluorescence spectra (classification error ≤1.0%). Adding the corresponding areas under the curves to the above combinations of spectra diminished errors to 0.6% and 0.3% or less, respectively. The best results for the compounded reflectance-plus-fluorescence spectra were obtained with fluorescence spectra excited at 415 nm with a total classification error of 0.2%; for the combination of the 405nm-excited fluorescence spectrum with another fluorescence spectrum, the best results were achieved for 385 nm, for which total relative classification error amounted 0.4%. The consideration of the area under the spectral curves further improved both classifiers, reducing the error to 0.0% in both cases. CONCLUSION: Spectrometric techniques combined with statistical processing are a promising tool to improve steatosis classification through a label free approach. However, statistical schemes here applied, might result complex for the everyday medical practice, the designed software including machine learning algorithms is able to render automatic classification of samples according to their steatosis grade with low error.

Diffuse reflectance spectroscopy accurately discriminates early and advanced grades of fatty liver in mice.

Nonalcoholic fatty liver disease (NAFLD) ranges from steatosis to nonalcoholic steatohepatitis and cirrhosis. Liver biopsy, considered the gold standard to diagnose NAFLD, shows significantly high rates of interobserver variability. Thus there is a need to develop tools that accurately categorize mild and advanced grades of steatosis in order to identify patients at higher risk of developing chronic liver disease. Diffuse reflectance spectroscopy (DRS) has proved to be useful in grading liver fibrosis and cirrhosis, without having been implemented for steatosis. We aim to categorize early and advanced stages of liver steatosis in a methionine-choline deficient (MCD) mouse model. C57bl/6 mice are fed either methionine-choline control or MCD diet during 2 or 8 weeks to induce mild and advanced steatosis. Liver samples are obtained and steatosis is evaluated by oil red O staining. Diffuse reflectance spectra are directly measured on ex vivo liver specimens, in a wavelength range of 400 to 800 nm. DRS is able to discriminate between early or advanced steatosis and healthy hepatic tissue with negligible error while showing high average sensitivity and specificity (0.94 and 0.95, respectively). Our results suggest that liver steatosis can be accurately evaluated by DRS, highlighting the importance of applied spectroscopic methods in assessing NAFLD.