Patent Description:
Because of a significant increase in its incidence in <NUM> - <NUM>, which grew from <NUM> million to <NUM> million across the world, diabetes makes one of the most serious threats to human health [<NUM>]. In <NUM>, diabetes was recognized as a global epidemic by the United Nations. Diabetes is one of the so called lifestyle diseases, i.e. common conditions that spread globally because of socio-economic development. According to the WHO report, it is estimated that the number of patients with diabetes (morbidity) has doubled in the recent <NUM> years, going up from <NUM>% in <NUM> to <NUM>% in <NUM>, which means that there are <NUM> million people with diabetes in the European Region of WHO. Each year, about <NUM>% of deaths are caused by a high blood sugar level, which means that <NUM> million people globally die because of diabetes and its complications [<NUM>]. Hence, it is essential to be able to detect diabetes in potential patients and then diagnose its complications and progression. This is why diagnostic techniques that enable the diagnosis of the disease (diabetes) and its monitoring are being developed across the world.

Diabetic nephropathy is a chronic kidney disease that develops in patients with diabetes, primarily because of frequent and long-term periods of hyperglycaemia. For patients with diabetes, it is important to regularly monitor kidney function and, if the first (early) symptoms of diabetes arise, implement treatment in order to stop the disease progression. The onset of diabetic nephropathy is asymptomatic and the glomerular filtration rate is close to normal in the early stage of the disease.

In many cases, diabetic nephropathy leads to end-stage kidney failure, i.e. dialysis. In Poland, there are over <NUM> thousand diabetic patients undergoing long-term dialysis therapy because of kidney failure. The stages of diabetic nephropathy are differentiated on the basis of the deteriorating glomerular filtration rate: GFR goes down from the level of <NUM> to <NUM>/min/<NUM><NUM> in the patients in the <NUM>nd stage of the disease to below <NUM>/min/<NUM><NUM> in the patients with end-stage kidney failure [<NUM>]. So far, the most important screening test for the patients who were not diagnosed with evident proteinuria on the basis of urine tests has been the level of albumin/creatinine in the early morning (or random) urine sample. Increased urinary excretion of albumin (albumin/creatinine ><NUM>/g) must be confirmed - <NUM> positive results are needed for the diagnosis. It is recommended that the first screening test for albuminuria be performed <NUM> years after they were diagnosed with the disease in the case of patients with type <NUM> diabetes, or at the moment of the diagnosis in the case of patients with type <NUM> diabetes. According to WHO (<NUM>), it is estimated that about <NUM>% of the new cases of diabetes are not diagnosed, which leads to the underestimation of the number of patients. In Poland, it is estimated that about <NUM>% of patients do not know that they have diabetes. The risk of diabetic nephropathy and cardio-vascular complications may range from two- to five-fold, depending on the patient's gender and age.

Another important social aspect is the quality of life of diabetic patients. The longer people live with undiagnosed diabetes, the worse is the starting point for treatment and the patient's condition at the onset of the therapy. According to the WHO diabetes report, screening tests for the early detection of diabetic complications should be included in all guidelines on the diagnosing and treatment of diabetes.

Raman spectroscopy has been extensively applied in biological tests, primarily in the examination of the structure of nucleic acids - DNA and RNA, protein complexes with nucleic acids (ribonucleoproteins) [<NUM>], lipids and carotenoids [<NUM>, <NUM>] and other chemical compounds (metabolites) [<NUM>]. In laboratory medicine, Raman spectroscopy is increasingly applied, primarily to monitor the drug level and identify pathogens [<NUM>], but more and more often it is used to diagnose tumours, including pharyngeal tumours [<NUM>], breast cancer [<NUM>, <NUM>], stomach cancer [<NUM>], skin cancer [<NUM>], osteoporosis [<NUM>], diabetes [<NUM>, <NUM>], atherosclerosis [<NUM>, <NUM>] and to diagnose kidney function [<NUM>, <NUM>]. Raman spectroscopy is also used in forensic tests to identify blood traces and other biological traces [<NUM>, <NUM>, <NUM>]. A new and unique application of Raman spectroscopy includes the analysis of molecular signatures of extracellular vesicles, which was performed on the samples of tumour cell line cultures and the samples of the patients' serum [<NUM>].

Microvesicles or extracellular vesicles (EVs) are tiny, spherical, membranous structures of the diameter of <NUM>-<NUM> which are released to the intercellular space [<NUM>]. This term refers both to the microvesicles that multiply directly from the cellular membrane during the cell's life cycle - ectosomes or during the cell's programmed death - apoptic blebs and the intercellular vesicles - exosomes released from multivesicular bodies [<NUM>]. Regardless of their different characteristics in terms of size (exosomes: <NUM>-<NUM>, ectosomes: <NUM>-<NUM>, apoptic blebs: <NUM>-<NUM>), microvesicles share a number of common features, such as a two-layer lipid membrane with lipids and nucleic acids inside these structures [<NUM>, <NUM>]. The cargo transported by microvesicles, which reflects the status of the excreting cell, is delivered to the target cell and takes an active part in intracellular signalling [<NUM>]. Extracellular vesicles may be obtained from all body fluids (e.g. blood, urine, saliva or cerebrospinal fluid) and their analysis may replace the invasive and expensive technique of traditional biopsy in the future. The diagnostics of genitourinary diseases may also be performed on the basis of the analysis of the material coming from urinary extracellular vesicles - UEVs [<NUM>, <NUM>, <NUM>].

A review provided by Lawson (<NUM>) presents the application of various populations of EVs as biomarkers of cardiovascular and metabolic diseases without indicating any specific methods for the isolation of EVs from clinical samples or defining Raman spectroscopy as a research technique [<NUM>]. Another review proposes that EVs serve as diagnostic markers for obesity or diabetes with the indication that urinary extracellular vesicles are the carriers of microRNA related to the disease [<NUM>].

In the scientific publication by Krafft (<NUM>) [<NUM>], extracellular vesicles were applied as diagnostic biomarkers in order to differentiate tumour patients. Raman spectroscopy was used to carry out a comparative analysis of extracellular vesicles collected from patients with tumours and from healthy individuals. To this end, two different fractions of EVs enriched with exosomes and ectosomes were isolated from blood serum and plasma using differential centrifugation. The change of the profile reflecting the protein structure (alpha-helix-rich, beta-sheet-rich) was used to detect prostate cancer.

The scientific publication of Brindh (<NUM>) [<NUM>] describes the application of Raman spectroscopy (for high wave numbers, HWVN) to characterise urine samples of healthy individuals, pre-cancer patients and cancer patients. It was observed that flavoproteins, metabolites, tryptophan and phenylalanine in urine are related to the differences in Raman spectra between the healthy group and the cancer group. It should be noted that the analysis was performed for the spectra with high wave numbers (<NUM> - <NUM>-<NUM>).

Saatkamp's publication (<NUM>) [<NUM>] provides information on the application of Raman spectroscopy to assess the concentration of urea and creatinine in urine, which may further be used to diagnose nephropathy.

In her publication, Kamińska (<NUM>) [<NUM>] presented studies indicating the relationship between the density of EVs, their size distribution and the progression of early kidney failure in patients with type <NUM> diabetes. The study involved patients with controlled and uncontrolled diabetes (additionally, with kidney failure and without kidney failure). The diameter and number of EVs was evaluated using the Tunable Resistive Pulse Sensing technique. The density of EVs was evaluated using a transmission electron microscope. It was demonstrated that urine is rich in EVs. Moreover, EVs were used to differentiate patients with controlled and uncontrolled diabetes, but the differentiation was based on the number and size distribution of EVs, which made it also possible to reflect the deterioration of kidney function suggesting that EVs may be applied as biomarkers of kidney failure. <CIT> (<NUM>. <NUM>) discloses urine exosome mRNAs and methods using them to detect diabetic nephropathy.

Considering the above-described state of the art, there is still demand for methods that would enable early, non-invasive and low-cost diagnosis of diabetes and its monitoring. The early diagnosis of diabetic nephropathy and the extent of its progression is especially important.

Unexpectedly, such a method was obtained in this invention.

The subject of the invention is a method of detecting and diagnosing diabetes wherein
the change in the composition of urinary extracellular vesicles (UEV) in a urine sample collected from a patient is examined, and this change confirms the presence of diabetes and its progression, wherein the method comprises the following stages:.

In an advantageous embodiment, in stage a), the urine sample is centrifuged at <NUM> x g for about <NUM> minutes.

In an advantageous embodiment, in stage a), the urine sample is concentrated using a large-pore dialysis membrane permeable for molecules with the average molecular weight below <NUM> kDa (MWCO), which is followed by washing.

In an advantageous embodiment, the washing solution contains colloidal silver (<NUM> of silver chloride and <NUM> of sodium dichloroisocyanurate per litre of the urine sample).

Unexpectedly, over the course of work that led to the development of this invention, it was determined that the change in the band intensity of the Raman spectrum, which is a consequence of the differences in the molecular composition of UEVs, as compared with UEVs of healthy individuals confirms the presence of the disease and makes it possible to differentiate between controlled and uncontrolled diabetes and diagnose its progression, in particular identify diabetic nephropathy and advanced kidney failure caused by diabetes.

The method of this invention makes it possible to differentiate between diabetic patients and the control group as well as between patients with controlled and uncontrolled diabetes by means of the analysis of urinary extracellular vesicles (UEVs) using Raman spectroscopy.

<FIG> shows the microphotograph of urinary extracellular vesicles (UEVs) obtained by means of centrifugation from a healthy individual (C) and a person with uncontrolled type <NUM> diabetes (UD); <FIG> shows the Raman spectra of the pooled samples obtained from patients with controlled type <NUM> diabetes (CD), patients with uncontrolled diabetes (UD) and <NUM> samples from the healthy control group (C); <FIG> shows the principle component analysis (PCA) of the Raman spectra obtained from patients with CD (■), UD (▲) and from the control group C (•) - the graphs are in non-nominal units and present the share of the component, a variable in this case (band intensity [a. ]), in the model of a covariance matrix.

The group under examination were patients with type <NUM> diabetes (n=<NUM>). The patients were divided into <NUM> groups according to the diabetes control level based on the level of glycated haemoglobin (HbA1C) following the <NUM> guidelines of the Polish Diabetes Association: the group with controlled diabetes (CD) (n=<NUM>) and the group with uncontrolled diabetes (UD) (n=<NUM>) where HbA1C > <NUM>%. The group of patients was compared with the control group (n=<NUM>). Table <NUM> shows the characteristics of the group under examination.

Additionally, the patients' history was collected, which included demographic data, arterial blood pressure, BMI, dietary habits and addictions (smoking and alcohol consumption) as well as the anti-diabetic treatment and drug therapy (potassium-sparing diuretics, loop diuretics, thiazide and thiazide-like diuretics, beta-blockers, ACEI, Ca antagonists, Na/K ATPase inhibitors, vasodilating diuretics, clopidogrel, acetosalicylic acid and statins), information on the surgeries and surgical procedures undergone by patients, including coronary artery bypass grafts (CABG) and percutaneous coronary interventions (PCI), as well as comorbidities and cancer. The patients diagnosed with the following conditions during the examination or history collection were excluded from the study:.

Samples of morning urine of about <NUM> were collected from the patients and the healthy volunteers. Initially, the urine was centrifuged at <NUM> x g for about <NUM> minutes in order to remove epithelial cells, bacteria and urinary deposits. Then, the urine was concentrated using hydrostatic filtration/dialysis (HDF), a method in which urine is filtrated using a dialysis membrane with large-diameter pores permeable to molecules whose molecular weight is below <NUM> kDa (MWCO). After volume reduction, the sample was washed using deionised water and, again, reduced to the volume of a few millilitres. In another variant, it is possible to use colloidal silver containing silver chloride and sodium dichloroisocyanurate in the amounts of, respectively, <NUM> and <NUM> per every litre of the urine sample, in order to chemically inactivate the remaining bacteria.

The deposit samples were centrifuged in Eppendorf tubes and stabilised in <NUM>% glutaraldehyde (cat. G5882, Sigma-Aldrich, St. Louis, USA) in <NUM> of cacodylic buffer (cat. C4945, Aldrich, St. Louis, USA) for <NUM> hours in room temperature, and then in <NUM>% solution of osmium tetrachloride (OsO<NUM>) for <NUM> hour. The samples were dehydrated in ethanol and embedded in PolyBed <NUM> in <NUM>. Snippets for analysis were placed on a mesh (<NUM> mesh grids). Next, the snippets were contrasted using uranyl acetate and lead citrate. The JEOL JEM 2100HT electronic microscope (JEOL Ltd. , Tokio, Japan) with accelerating voltage of 80kV was used for observation. This stage is presented in <FIG>.

Raman spectra were registered using the Renishaw InVia Raman spectrometer equipped with an optical confocal microscope with the Leica N PLAN EPI dry lens (100x, NA <NUM>). The laser emitting the light of the wavelength of <NUM> was cooled using air; the laser power in the sample position was about <NUM> mW. The CCD detector was cooled thermoelectrically to the temperature of -<NUM>. A drop of the UEV suspension was placed on the CaF<NUM> window and left there until the water evaporated. Each dried sample was measured in at least <NUM> randomly selected places. Eventually, <NUM> scans with the exposure time of <NUM> and the resolution of about <NUM>-<NUM> were collected from each place. The spectrometer was calibrated on the basis of the location of the Raman band of the silicon plate inside the device. A principle component analysis (PCA) was carried out using the Unscrambler X <NUM> software (CAMO AS, Norway). Prior to PCA, Raman spectra were adjusted by cutting off the baseline, and, subsequently, they were smoothed and normalised. The registered Raman spectra were presented in <FIG>. Table <NUM> shows the intensity of the characteristic bands that are important for the differentiation of individual sample groups, which was concluded on the basis of PCA. <FIG> shows the principle component analysis of the Raman spectra registered for the samples obtained from the patients. It clearly shows that individual points gather in clusters that include the individual groups under examination.

To determine the value of RI, which differentiates the CD and UD group from the control group C in an arbitrary way, the following formula was used: <MAT> where:.

The probability value (p) was calculated in the OriginPro <NUM> programme (according to the algorithm for the Kruskal-Wallis test).

The Kruskal-Wallis test statistics in the OriginPro <NUM> programme is calculated according to the following formula: <MAT>.

On the basis of the equation, IR for group C, CD and UD was determined: <MAT> <MAT> <MAT>.

The values were provided with the approximation error experimentally determined. On the basis of this, the threshold value for diabetes was determined: <MAT>.

An additional analysis of the variability of the urinary extracellular vesicle spectra using Raman spectroscopy was performed for the extended group of patients with type <NUM> diabetes (n=<NUM>) and various degrees of renal impairment in diabetic nephropathy defined according to the value of the glomerular filtration rate (GFR).

Clinical and epidemiological data can be found in Table <NUM>.

The groups were compared between one other using ANOVA or Kruskal-Wallis tests.

The table above shows that the patients with the highest level of renal impairment (Group <NUM> and Group <NUM>) were significantly different from the remaining groups of patients with regard to:.

Next, the Raman spectra were registered (just like before) for the range of <NUM>-<NUM>-<NUM>, for the urinary extracellular vesicles isolated from the morning urine samples (<NUM>-<NUM>) collected from the patients, individually for each patient.

Then the spectra were divided into <NUM> groups according to the criterion of renal impairment (GFR), the spectra were averaged and the average spectra for each group were plotted. The graphs can be found in <FIG>, which shows the recording of the averaged spectra for <NUM> groups of patients classified according to the degree of kidney failure: <NUM>) <NUM>-<NUM>/min/<NUM><NUM>; <NUM>) <NUM>-<NUM>/min/<NUM><NUM>; <NUM><NUM>-<NUM>/min/<NUM><NUM> ; <NUM>) <NUM>-<NUM>/min/<NUM><NUM> ; <NUM> <<NUM>/min/<NUM><NUM>.

On the basis of the spectra determined, the analysis of the average values of the area under the curve (AUC) was carried out for the selected bands representing metabolites present in urinary extracellular vesicles of the patients with diabetes and various degrees of renal impairment. Additionally, the analysis involved the values of the average intensity of the selected bands.

On the basis of the biochemical parameters determined and the analysis of Raman spectra, the correlation analysis using a parametric test (a Pearson correlation test) for the AUC values in individual ranges and the biochemical marker concentration values was performed. On the basis of the calculations performed, it can be concluded that, for the patients with diabetes, the values for the Raman spectrum band corresponding to nucleic acids (DNA) correlate significantly with:.

which corresponds to the level of progression of kidney failure.

Moreover, for the patients with diabetes, the values of the Raman spectrum corresponding to:.

correlate significantly (positive correlation) with the concentration of triglycerides (TG), which corresponds to the level of progression of dyslipidaemia in diabetes.

The additional analyses carried out for the group of patients with diabetic nephropathy which show the application of the introduced RI value to the assessment of the degree of renal impairment in patients with diabetes.

The following equation was used to determine the value of RI, which differentiates, in an arbitrary way, the CD (controlled diabetes) and UD (uncontrolled diabetes - uncontrolled blood sugar level) groups from the control group C: <MAT> where:.

On the basis of the area under the curve (AUC), the parameters determined and the average band intensity (I), RI for individual groups of patients (Table <NUM>) was determined using formula (<NUM>).

On the basis of the RI values calculated, it was established that the threshold value for diabetic nephropathy was RI <<NUM>.

Claim 1:
A method of detecting and diagnosing diabetes wherein the change in the composition of urinary extracellular vesicles (UEV) in a urine sample collected from a patient is examined, and this change confirms the presence of diabetes and its progression, wherein the method comprises the following stages:
a) Urinary extracellular vesicles (UEV) are isolated from the urine sample,
b) Raman spectra are registered and the analysis of the distribution of the intensity of characteristic bands is performed, wherein the characteristic bands in the Raman spectrum are located in the following ranges: from <NUM>-<NUM> to <NUM>-<NUM>, from <NUM>-<NUM> to <NUM>-<NUM>, from <NUM>-<NUM> to <NUM>-<NUM> and from <NUM>-<NUM> to <NUM>-<NUM>,
c) If it is confirmed that the value of RI for the intensity of the Raman spectrum is lower than the value of RI for a Raman spectrum obtained in an identical way for a sample collected from a healthy individual, the patient is diagnosed with diabetes, wherein RI is calculated on the basis of the following formula: <MAT> where:
I Amid I is the value of the band intensity for Amid I (<NUM> - <NUM>-<NUM>)
I Phenylalanine is the value of the band intensity for phenylalanine (<NUM> - <NUM>-<NUM>)
I Lipids is the value of the band intensity for lipids (<NUM> - <NUM>-<NUM>)
a is the weight factor determined as the quotient of statistical significance (p)
for the difference between the intensity of bands I Lipids and I Amid I between the group of patients and the group of healthy individuals calculated on the basis of the following formula:<MAT>
wherein it is accepted that the values of RI:
- for the group of healthy individuals are above <NUM>, preferably from <NUM> to <NUM>,
- for patients with diabetes are below <NUM>,
- for the group of patients with controlled diabetes are from <NUM> to <NUM>,
- for the group of patients with uncontrolled diabetes are from <NUM> to <NUM>,
- for the group of patients with advanced renal impairment caused by diabetes with the glomerular filtration rate (GFR) below <NUM>/min/<NUM><NUM> are below <NUM>.