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    • 专利摘要:
    BIOMARKERS ASSOCIATED WITH PRE-DIABETES, DIABETES AND / OR AN AFFECTION RELATED TO DIABETES. It is a biomarkers for pre-Diabetes, an invention that provides Diabetes and / or a condition related to Diabetes and the methods for using them, which include the biomarkers in Tables 1 and 2 as peroxiredoxin-2, subunit B of the subcomponent Complement clq, sulfhydryl oxidase 1 and apolipoprotein A-IV.
    公开号:BR112013006764B1
    申请号:R112013006764-0
    申请日:2011-09-20
    公开日:2020-11-03
    发明作者:Thomas Stoll；Scott Bringans；Kaye Winfield；Tammy Casey；Wendy Davis；Kirsten Peters；Timothy Davis；Richard Lipscombe
    申请人:Proteomics International Pty Ltd；
    IPC主号:
    专利说明:

    FIELD OF THE INVENTION
    The invention relates to biomarkers associated with pre-Diabetes, Diabetes and Diabetes-related conditions, such as diabetic nephropathy, to methods of using biomarkers to determine the risk at which an individual will develop pre-Diabetes, Diabetes and conditions related to Diabetes, methods of screening a population to identify people at risk of developing pre-Diabetes, Diabetes and Diabetes-related conditions and drug targets for pre-Diabetes, Diabetes and Diabetes. BACKGROUND OF THE INVENTION
    Diabetes mellitus is a chronic disease and one of the main public health problems at the present time. Worldwide, there is an ever-growing population of patients with diabetes who represent a major financial burden on health systems. The worldwide prevalence of diabetes for all age groups was estimated to be 2.8% in 2000 and 4.4% in 2030. The total number of people with diabetes is projected to rise from 171 million in 2000 to 366 million in 2030. In 2002, the prevalence of diabetes in the Australian population was 7.4% at age 25 and onwards, and the number of Australians with diabetes has tripled since 1981.
    Type 2 diabetes is by far the most common, for example, affecting 90 to 95% of the US diabetic population. The prevalence of diabetes mellitus increases with age, and the numbers of older people with diabetes are expected to increase as the elderly population increases in number. Along with the increasing rate of diabetes, there is also a greater prevalence of impaired glucose metabolism, which is associated with an increased risk of heart disease and diabetes. Diabesity is a term that covers the predominance of diabetes, obesity, impaired glucose metabolism and the associated risk factors for hypertension and abnormal plasma lipid profiles (dyslipidemia). The "diabetes epidemic" will continue, even if obesity levels remain constant. Given the increasing prevalence of obesity, these situations are less likely to underestimate the future prevalence of diabetes.
    Diabetes mellitus is a condition in which the body cannot maintain normal blood glucose levels. Most cases of diabetes mellitus fall into three broad categories: Type 1, Type 2 and gestational diabetes. Type 1 diabetes results from the body's failure to produce insulin, and currently requires the person to inject insulin. Type 2 diabetes results from insulin resistance, a condition in which cells fail to use insulin properly, sometimes combined with an absolute insulin deficiency.
    Type 2 diabetes can usually be controlled in the first instance by regular exercise and diet. Pills and, eventually, insulin injections may be needed as the disease progresses. Over time, high blood glucose levels can damage blood vessels and nerves. These diabetes complications can cause damage to the eyes, nerves and kidneys, and increase the risk of heart attack, stroke, impotence and foot problems. This damage can happen before an individual knows that he or she has diabetes if left undetected for a long time. Therefore, it is important to diagnose and control diabetes and its complications at a very early stage.
    Diabetes is also a major cause of kidney disease (nephropathy) in developed countries, and is responsible for huge costs on dialysis. 10% to 20% of people with diabetes will die from kidney (kidney) failure. The reasons behind the complication of nephropathy in diabetes are complex, and include the toxic effects of high glucose levels; high blood pressure; abnormal lipid levels and abnormalities of small blood vessels. The cumulative result is that there is a thickening of the glomeruli in the kidney that allow the protein (albumin) to be excreted in the urine.
    Diabetes has become the most common cause of end-stage renal failure (ESRF) in 40 to 50% of ESRD cases, and Australia's annual Medicare expenses are the highest for patients with diabetes-related ESRF compared to all other diagnoses primary ESRD. Up to a third of adults with newly diagnosed Type 2 diabetes already have chronic kidney disease, and data suggest that in many of these patients, it may have developed in the course of the pre-diabetic state. The disease is progressive and affects more men than women.
    Diabetic nephropathy is detected primarily by measuring the amount of albumin excreted in the urine (albuminuria). Albuminuria is usually measured using the albumin to creatinine ratio (ACR). This is the ratio between albumin and creatinine in the urine. The ratio considers the concentration of albumin in relation to the glomerular filtration rate, which is determined by the amount of creatinine in the urine. Albuminuria is defined as: ACR> 2.5mg / mmol (men) or> 3.5mg / mmol (women).
    Despite the various studies and algorithms that have been used to assess the risk of Diabetes and related conditions, there remains a need for accurate methods for assessing such risks or conditions that can be readily adapted by primary care physicians who are most likely to encounter pre - undiagnosed diabetic or early diabetic.
    Consequently, there remains a need for convenient and relatively inexpensive methods to screen people at risk of developing pre-Diabetes, Diabetes and / or a Diabetes-related condition and to monitor patients with Pre-Diabetes, Diabetes and / or a disease-related condition Diabetes. Such methods could be used to screen a large population to identify people at risk for diabetes, to test a single person to determine that individual's risk of developing diabetes, to monitor the health of diabetes patients and to assess the effectiveness of interventions designed to treat diabetes, pre-diabetes and / or related conditions. There is also a need to identify new drug targets for pre-Diabetes, Diabetes and / or Diabetes-related conditions that include protein drug targets. The identification of new drug targets will allow the development of new interventions for pre-Diabetes, Diabetes and / or conditions related to Diabetes.
    It is against this background and the problems and difficulties associated with them that the present invention was developed. SUMMARY OF THE INVENTION
    In one aspect, the present invention provides a method for evaluating a patient for pre-Diabetes, Diabetes and / or a Diabetes-related condition, which comprises measuring at least one biomarker in a patient sample, wherein said at least a biomarker is selected from the list of biomarkers in Table 1 or 2. TABLE 1
    TABLE 2


    In another aspect, the present invention provides a kit that comprises reagents for measuring at least one biomarker in a patient sample, wherein said at least one biomarker is selected from the list 5 of biomarkers in Table 1 or 2.
    In another aspect, the present invention provides a computer-readable medium that has computer-executable instructions for evaluating a patient for pre-Diabetes, Diabetes and / or a condition related to Diabetes, the computer-readable medium comprising a routine, stored in a computer readable medium and adapted to be executed by a processor to store biomarker measurement data that represents at least one biomarker selected from the list of biomarkers in Table 1 or 2.
    In another aspect, the present invention provides a method for evaluating a treatment for pre-Diabetes, Diabetes and / or a Diabetes-related condition in a patient comprising the measurement of at least one biomarker, in a sample of the patient undergoing treatment , selected from the list of biomarkers in Table 1 or 2, at least twice during the course of treatment.
    In another aspect, the present invention provides a method for assessing a patient's risk of developing pre-Diabetes, Diabetes and / or a Diabetes-related condition, which comprises measuring at least one biomarker, in a patient sample, selected from the list of biomarkers in Table 1 or 2.
    In another aspect, the present invention provides a method of monitoring pre-Diabetes, Diabetes and / or a Diabetes-related condition in a patient that comprises measuring at least one biomarker in a patient sample, selected from the list of biomarkers in Table 1 or 2, and compare the measurement obtained with another measurement of at least one biomarker.
    In another aspect, the present invention provides a method for diagnosing or identifying pre-Diabetes, Diabetes and / or a Diabetes related condition in a patient comprising the measurement of at least one biomarker, in a patient sample, selected from among list of biomarkers in Table 1 or 2.
    In another aspect, the present invention provides a method of differentially diagnosing kidney disease from conditions that also cause proteinuria in a patient comprising the measurement of at least one biomarker, in a patient sample, selected from the list of biomarkers. in Table 1 or 2.
    In another aspect, the present invention provides a method to differentially diagnose subclasses or stages of pre-Diabetes, Diabetes and / or a condition related to Diabetes in a patient comprising the measurement of at least one biomarker in a patient sample , selected from the list of biomarkers in Table 1 or 2.
    In another aspect, the present invention provides a test system comprising: (i) a means of obtaining data from test results that represent the levels of at least one biomarker selected from the list of biomarkers in Table 1 or 2, in a sample of the patient; (ii) means for collecting and tracking test result data generated in step (i); (iii) means for calculating a pre-Diabetes, Diabetes and / or Diabetes-related risk index value from test result data, where said risk index value is representative of the risk an individual developing or having pre-Diabetes, Diabetes and / or a condition related to Diabetes; and (iv) means to report said risk index value.
    In another aspect, the present invention provides a method for classifying or grouping a population of individuals, which comprises: obtaining pre-Diabetes, Diabetes and / or a Diabetes related risk data for individuals in said population; and classifying individuals within the population in relation to the remaining individuals in the population, or dividing the population into at least two groups, based on the factors that comprise said risk index data obtained.
    In another aspect, the present invention provides a method for estimating a substitute outcome for pre-Diabetes, Diabetes and / or a condition related to Diabetes in a patient, the method comprising: measuring at least one biomarker from the list of biomarkers in the Table 1 or 2; and estimate a substitute outcome for pre-Diabetes, Diabetes and / or a condition related to Diabetes in the patient based on said measure.
    In another aspect, the present invention provides a method for estimating a patient's risk of developing pre-Diabetes, Diabetes and / or a Diabetes related condition, which comprises measuring at least one biomarker in a patient sample, in which the said at least one biomarker is selected from the list of biomarkers in Table 1 or 2.
    In another aspect, the present invention provides a method for monitoring a patient's risk of developing pre-Diabetes, Diabetes and / or a Diabetes-related condition, which comprises measuring at least one biomarker in a patient sample, in which the said at least one biomarker is selected from the list of biomarkers in Table 1 or 2.
    In another aspect, the present invention provides a method for diagnosing or identifying a patient with pre-Diabetes, Diabetes and / or a condition related to Diabetes, which comprises measuring at least one biomarker in a patient sample, in which said at least one biomarker is selected from the list of biomarkers in Table 1 or 2.
    In another aspect, the present invention provides a method for monitoring therapy or intervention for pre-Diabetes, Diabetes and / or a Diabetes related condition, which comprises measuring at least one biomarker in a patient sample, in which the said at least one biomarker is selected from the list of biomarkers in Table 1 or 2.
    In another aspect, the present invention provides a method to differentially diagnose a disease state or subclass of pre-Diabetes, Diabetes and / or a condition related to Diabetes, which comprises measuring at least one biomarker in a sample of the patient, in which said at least one biomarker is selected from the list of biomarkers in Table 1 or 2.
    In another aspect, the present invention provides a method for treating pre-Diabetes, Diabetes and / or a condition 10 related to Diabetes in a patient, which comprises: estimating the risk, for the patient, to develop pre-Diabetes, Diabetes and / or a Diabetes related condition using at least one biomarker from Table 1 or 2, and treating the patient when identified as being at high risk for pre-Diabetes, Diabetes and / or a Diabetes related condition with a treatment regimen to delay or prevent the onset of pre-Diabetes, Diabetes and / or a condition related to Diabetes.
    In another aspect, the present invention provides a method for classifying or grouping a patient population, which comprises: obtaining data representing a risk score for pre-Diabetes, Diabetes and / or a Diabetes-related condition for patients comprised within of said population, in which said risk score 25 is calculated using at least one biomarker from Table 1 or 2, and classifying patients within the population in relation to the remaining individuals in the population, or dividing the population into at least two groups, based on the factors that comprise said 30 risk count data obtained.
    In another aspect, the present invention provides a method for identifying or evaluating an agent to treat or reduce the risk of developing pre-Diabetes, Diabetes and / or a condition related to Diabetes, which comprises: (i) contacting cells that express at least one biomarker from Table 1 or 2, with a putative agent; and (ii) comparing the expression and / or levels of at least one biomarker from Table 1 in cells, prior to contact with the putative agent with the expression and / or levels of at least one biomarker from Table 1 or 2 in cells after contact with the putative agent; where a change in level or expression identifies the agent as an agent for treating pre-Diabetes, Diabetes and / or a condition related to Diabetes.
    Thus, another aspect of the present invention provides the use of at least one biomarker in Table 1 or 2 as a drug target for pre-Diabetes, Diabetes and / or a condition related to Diabetes.
    In another aspect, the present invention provides a method for treating or reducing the risk of developing pre-Diabetes, Diabetes and / or a Diabetes related condition in a patient that comprises administering an effective amount of an agent adapted to change the expression or the level of at least one biomarker in Table 1 or 2 to the patient.
    In another aspect, the present invention provides the use of an agent adapted to change the expression or level of at least one biomarker in Table 1 or 2 to prepare a medication to treat or reduce the risk of developing pre-Diabetes, Diabetes and / or a condition related to Diabetes. BRIEF DESCRIPTION OF THE DRAWINGS
    The following Detailed Description of the Invention, given by way of example, but not intended to limit the invention to the specific modalities described, can be understood in conjunction with the attached Figures, in which:
    Figure 1 is a table that lists the biomarker protein data obtained from three studies regarding the presence of diabetic nephropathy in patients with diabetes measured by monitoring multiple reactions (MRM);
    Figure 2 is a series of box mustache wire diagrams for each biomarker listed in Figure 1 of the FDS1 study (box diagram on the left: diabetic group; box diagram on the right: diabetic group with severe nephropathy; geometric axis x : protein / peptide; y-axis: Relative abundance ratio; peptide sequences: ATA = ATAVVDGAFK; TVA = TVAACNLPIVR; EYC = EYCGVPGDGDEELLR; LEP = LEPYADQLR; and ISA = ISASAEELR)
    Figure 3 is a series of box mustache wire diagrams for each biomarker listed in Figure 1 of the FDS2 study (box diagram on the left: diabetic group; box diagram on the right: diabetic group with severe nephropathy; geometric axis x : protein / peptide; y-axis: Relative abundance ratio; peptide sequences: IAF = IAFSATR; LEP = LEPYADQLR; ISA = ISASAEELR; ALA = ALAQCAPPPAVCAELVR; and FLN = FLNVLSPR; DAL DALSSVQESQVAQQAR; TVR = EGV; GDI = GDIGETGVPGAEGPR; TGD = TGDIVEFVCK; LVY = LVYPSCEEK);
    Figure 4 is a series of box mustache wire diagrams for each biomarker listed in Figure 1 of the BDS study (box diagram on the left: diabetic group; box diagram on the right: diabetic group with severe nephropathy; geometric axis x : protein / peptide; y-axis: Relative abundance ratio; peptide sequences: LVG = LVGGDNLCSGR; IWL = IWLDNVR; SVS SVSLPSLDPASAK; and TEV = TEVIPPLIENR); and
    Figure 5 is a table that lists the biomarker protein data obtained from the BDS study in relation to patients with diabetic nephropathy and healthy patients as measured by MRM. DETAILED DESCRIPTION OF THE INVENTION
    The present invention relates to the identification of biomarkers associated with pre-Diabetes, Diabetes and / or conditions related to Diabetes, such as diabetic nephropathy. Consequently, the present invention features methods for identifying patients who are at risk of developing pre-Diabetes, Diabetes and / or Diabetes-related conditions, which includes those patients who are asymptomatic or only exhibit non-specific indicators of Pre-Diabetes, Diabetes and / or conditions related to Diabetes by detecting the biomarkers revealed in this document. These biomarkers are also useful for monitoring patients undergoing pre-Diabetes, Diabetes and / or Diabetes-related treatments and therapies, and for selecting or modifying therapies and treatments that would be effective, and patients who have pre-Diabetes, Diabetes and / or conditions related to Diabetes, in which the selection and use of such treatments and therapies delay the progression of pre-Diabetes, Diabetes and / or conditions related to Diabetes, or prevent its onset. The present invention also features new drug targets for pre-Diabetes, Diabetes and / or Diabetes-related conditions that comprise at least one of the biomarkers in Table 1 or 2. Definitions
    "Agents to treat or reduce the risk of developing pre-Diabetes, Diabetes and / or a condition related to Diabetes" include: insulin, such as mature insulin, pro-insulin and soluble c-peptide (SCp), fast-acting forms of insulin, regular insulin, intermediate-acting insulin and long-acting forms of insulin; hypoglycemic agents; anti-inflammatory agents; lipid reducing agents; antihypertensive drugs such as calcium channel blockers, beta-adenergic receptor blockers, cyclooxygenase-2 inhibitors that include COX-2 inhibitor prodrugs, agiotensin system inhibitors that include angiotensin II receptor blockers (ARBs), inhibitors of ACE and renin inhibitors that include amino acids and derivatives thereof, peptides and derivatives thereof, and antibodies to renin.
    "Angiotensin II antagonists" are compounds that interfere with angiotensin II activity by binding to angiotensin II receptors and interfering with their activity and include peptide compounds and non-peptide compounds. Most angiotensin II antagonists are slightly modified congeners, in which agonist activity is attenuated by the replacement of phenylalanine at position 8 with some other amino acid. Examples of angiotensin II antagonists include: peptide compounds (for example, saralasin, angiotensin- octapeptide (1-8) and related analogs); N-substituted imidazole-2-one; imidazole acetate derivatives that include 2-N-butyl-4-chloro-1- (2-chlorobenzyl) imidazole-5-acetic acid; 4,5,6,7-tetrahydro-1H-imidazo [4,5-c] pyridine-6-carboxylic acid and analogous derivatives; N2-tetrazole beta-glucuronide analogs; substituted pyrroles, pyrazoles, and triazoles; heterocyclic derivatives and phenol, such as 1,3-imidazoles; 7-membered ring geterocycles fused with imidazole; antibodies to angiotensin II; and aralkyl imidazole compounds, such as biphenyl methyl substituted imidazoles; ES8891 (N-morpholinoacet11 - (- 1-naphthyl) -L-alani-1- (4, thiazolyl) -L-alanyl (35.45) -4-amino-3-hydroxy-5-cyclohexapentanoyl - N - hexylamide); SKF108566 (E-alpha-2- [2-butyl-1- (carboxy phenyl) methyl] 1H-imidazola-5-yl [methylan-e] -2-thiophenenepropanoic); Losartan (DUP753 / MK954) and Remikirin.
    "Angiotensin-converting enzyme inhibitors (ACE)" include amino acids and derivatives thereof, peptides, which include di- and tri-peptides and antibodies to ACE that intervene in the renin-angiotensin system by inhibiting ACE activity, reducing or eliminating , thus, the formation of angiotensin II vasoconstrictive substance. Classes of compounds known to be useful as ACE inhibitors include acylmercapto and mercaptoalkanoyl prolines, such as captopril and zofenopril, carboxyalkyl dipeptides, such as enalapril, lisinopril, quinapril, ramipril, and perindopril, imitations of carboxyalkyl dipeptide, such as cilazapril prolines, such as fosinopril and trandolopril.
    "Anti-inflammatory" agents include: Alclofenac; Alclomethasone dipropionate; Algestone Acetonide; Alpha Amylase; Amcinafal; Amcinafide; Amfenac Sodium; Amiprilose Hydrochloride; Anakinra; Anirolac; Anitrazafen; Apazona; Balsalazide Disodium; Bendazac; Benoxaprofen; Benzidamine hydrochloride; Bromelains; Broperamol; Budesonide; Carprofen; Cycloprophen; Cintazona; Cliprofen; Clobetasol propionate; Clobetasone Butyrate; Clopirac; Cloticasone Propionate; Cormetasone Acetate; Cortodoxona; Deflazacort; Desonide; Deoxymethasone; Dexamethasone dipropionate; Diclofenac Potassium; Diclofenac Sodium; Diflorasone diacetate; Diflumidone Sodium; Diflunisal; Difluprednate; Diftalone; Dimethyl sulfoxide; Drocinonide; Endrisone; Enlimomab; Enolicam Sodium; Epirizol; Etodolac; Etophenamate; Felbinac; Fenamol; Fenbufen; Fenclofenac; Fenclorac; Fendosal; Fenpipalona; Fentiazac; Flazalone; Fluazacort; Flufenamic Acid; Flumizol; Flunisolide Acetate; Flunixin; Flunixin Meglumine; Fluocortin Butyl; Fluorometholone acetate; Fluquazone; Flurbiprofen; Fluretofen; Fluticasone Propionate; Furaprofen; Furobufen; Halcinonide; Halobetasol Propionate; Halopredone acetate; Ibufenac; Ibuprofen; Ibuprofen Aluminum; Ibuprofen Piconol; Ilonidap; Indomethacin; Indomethacin Sodium; Indoprofen; Indoxol; Intrazole; Isoflupredone acetate; Isoxepac; Isoxicam; Ketoprofen; Lofemizole hydrochloride; Lornoxicam; Loteprednol etabonate; Sodium meclofenamate; Meclophenamic Acid; Meclorisone Dibutyrate; Mefenamic Acid; Mesalamine; Meseclazone; Methylprednisolone sulfate; Morniflumate; Nabumetone; Naproxen; Naproxen Sodium; Naproxol; Nimazone; Olsalazine Sodium; Orgotein; Orpanoxin; Oxaprozin; Oxyphenbutazone; Paraniline Hydrochloride; Pentosan Sodium Polysulfate; Fenbutazone Sodium Glycerate; Pirfenidone; Piroxicam; Piroxicam cinnamate; Piroxicam Olamine; Pirprofen; Prednazate; Prifelone; Prodolic Acid; Proquazone; Proxazole; Proxazole citrate; Rimexolone; Romazarit; Salcolex; Salnacedina; Salsalate; Salicylates; Sanguinarium chloride; Seclazone; Sermetacin; Sudoxicam; Sulindac; Suprofen; Talmetacin; Talniflumate; Talosalate; Tebufelone; Tenidap; Tenidap Sodium; Tenoxicam; Tesicam; Tesimide; Tetridamine; Tiopinac; Tixocortol pivalate; Tolmetin; Tolmetin Sodium; Triclonide; Triflumidate; Zidomethacin; Glucocorticoids; Zomepirac Sodium, aspirin, cytokine inhibitors, such as cytokine antagonists (eg, IL-6 receptor antagonists), aza-alkyl lysophospholipids (AALP), and Tumor Necrosis Factor (TNF-alpha) inhibitors, such such as anti-TNF-alpha antibodies, soluble TNF receptor, TNF-alpha, antisense nucleic acid molecules, multivalent guanylhydrazone (CNI-1493), N-acetylcysteine, pentoxifylline, oxpentiphyline, carbocyclic nucleoside analogs, TNF inhibitors -alpha and Dexanabinol, such as Etanercept and Infliximab.
    "Beta-adrenergic receptor blocking agents" antagonize the cardiovascular effects of catecholamines in angina pectoris, hypertension, and cardiac arrhythmias and include atenolol, acebutolol, alprenolol, befunolol, betaxolol, bunitrolol, carteolol, celiprolunol, levololol, hydrololol, labyrololol, labyrintholol, cholesterol, levololol, toluene, hydrololol, toluene, hydrololol, toluene, hydrololol, toluene, hydrololol, toluene, hydrololol, toluene, hydrololol, cholesterol, hydrololol, cholesterol, hydrololol, cholesterol. mepindolol, methipranol, metindol, metoprolol, metrizoranolol, oxprenolol, pindolol, propranolol, practolol, practolol, sotalolnadolol, tiprenolol, tomalolol, timolol, bupranolol, penbutolol, trimepranol, 2- (3- (1,1-dimethyl-2- -hydroxypropoxy) - 3-pyridenecarbonitrileHCl-, l-butylamino-3- (2,5-dichlorophenoxy -) - 2-propanol, l-isopropylamino-3- (4- (2-cyclopropylmethoxyethyl) phenoxy) -2-propanol, 3 -isopropylamino-1- (7-methylindan-4-yloxy) -2-butanol, 2- (3-t-butylamino-2-hydroxy-propylthio) -4- (5-carbamoyl-2-thienyl) thiazole and phthalide 7- (2-hydroxy-3-t-butylaminpropoxy). "Calcium channel blockers" belong to one of the three main chemical groups of drugs, dihydropyridines, such as nifedipine, phenyl alkyl amines, such as verapamila, and benzothiazepines, such as diltiazem. Other calcium channel blockers useful according to the invention include aminone, amlodipine, benciclane, felodipine, phendiline, flunarizine, isradipine, nicardipine, nimodipine, perhexylene, galopamil, tiapamil and thiapamyl, phenylin, barbiturates, and peptide analogs, and peptides omega-conotoxin, and omega-agatoxin.
    "Diabetes" includes Type 1 Diabetes, both autoimmune and idiopathic, Type 2 Diabetes and Gestational Diabetes. Diabetes can be characterized by recurrent and persistent hyperglycemia and can be diagnosed by increased blood glucose levels and 6.5% glycated hemoglobin). According to the current definition, with two fasting glucose measurements above 126 mg / dl (7.0 mmol / 1) the diagnosis for Diabetes Mellitus is considered.
    "Diabetes-related condition" includes any condition or disease that is a result or complication of, or is otherwise correlated or associated with, Diabetes that includes a condition caused by higher than normal blood glucose levels and a selected condition among the list consisting of: hypoglycemia, diabetic ketoacidosis, diabetic neuropathy, kidney disease including diabetic nephropathy, cardiovascular disease, diabetic stroke and retinopathy and arteriovascular disease.
    "Biomarker" in the context of the present invention encompasses, without limitation, the proteins in Table 1 and 2, in fact, measurements thereof; nucleic acids that encode the proteins in Table 1 or 2; degradation products and protein metabolites in Table 1 or 2; polymorphisms, mutations, variants, modifications, subunits, peptides (such as those in Table 3) and protein fragments in Table 1 or 2; and protein-binding complexes that include the proteins in Table 1 or 2. Biomarkers can also include proteins with at least 50%, 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98 % or 99% identity or similarity to the proteins in Table 1 or 2, as well as mutated forms of the proteins in Table 1 or 2 and nucleic acids that encode such mutations. Biomarkers can be used to calculate mathematical indices or other measurements, which include time trends and differences that are useful in relation to the present invention.
    "Gestational diabetes" refers to glucose intolerance during pregnancy. This condition results in a high blood sugar level that starts or is first diagnosed during pregnancy.
    "Hypoglycemic" agents include oral hypoglycemic agents and include, without limitation, first generation sulfonylurea: Acetoexamide, Chlorpropamide, Tolbutamide; second generation sulfonylurea: Glipizide, Gliburide, Glimepiride; Biguanides: Metformin; Alpha-glucosidase inhibitors: Acarbose, Miglitol, Thiazolidinediones: Rosiglitazone, Pioglitazone, Troglitazone; Meglitinides: Repaglinide; and other hypoglycemics such as Acarbose; Buformin; Butoxamine hydrochloride; Camiglibose; Ciglitazone; Englitazone Sodium; Darglitazone Sodium; Etoformin Hydrochloride; Gliamilide; Glibomuride; Glycetanil Sodium glycidazide; Glyphlumide; Glucagon; Gliexamide; Sodium Glimidine; Glioctamide; Gliparamide; Linogliride; Linogliride fumarate; Methyl Palmoxyrate; Sodium Palmoxyrate; Pirogliride tartrate; Human proinsulin; Seglitide acetate; Tolazamide; Tolpirramide; Zopolrestato.
    "Altered fasting blood glucose" (IFG) is a pre-diabetic condition associated with a blood glucose level that is higher than normal, but not high enough to be classified as Diabetes. A patient with IFG may have a fasting blood sugar (glucose) level below or equal to 125mg / l, between 100 and 125mg / dl or between 105 and 125mg / dl.
    The term "identity" used in this document refers to a relationship between the sequences of two or more molecules, as determined by comparing the sequences. "Identity" also means the degree of sequence association between the polypeptide or nucleic acid molecule sequences, as the case may be, as determined by compatibility between nucleotide strings or amino acid sequences. "Identity" measures the percentage of identical combinations between two or more sequences with interval alignments directed by a particular mathematical model of computer programs.
    "Decreased glucose tolerance" (IGT) is a pre-diabetic condition associated with a blood glucose level that is higher than normal, but not high enough to be classified as Diabetes. A patient with IGT may have two-hour glucose levels of 140 to 199 mg / dl (7.8 to 11.0 mmol) in the 75-g oral glucose tolerance test.
    "Lipid-lowering agents" include gemfibrozil, colistiramine, colestipol, nicotinic acid, and HMG-CoA reductase inhibitors, such as simvastatin, lovastatin, pravastatin sodium, fluvastatin, atorvastatin and cerivastatin.
    The term "measure" and variants such as "measure", as used herein, in relation to the biomarkers described in this document refer to determining the presence and / or quantity of a given biomarker.
    "Pre-Diabetes" is a 'state in which some, but not all diagnostic criteria for Diabetes are met. This includes conditions in which blood glucose levels are between normal and diabetic levels, conditions in which patients suffer from impaired glucose tolerance (IGT), altered fasting glucose (IFG) and / or glycated hemoglobin between 5.7 and 6, 4%.
    A "sample" in the context of the present invention is a biological sample isolated from a patient and may include, by way of example and without limitation, all blood, blood fraction, serum, plasma, blood cells, endothelial cells, tissue biopsies , lymphatic fluid, ascites fluid, interstitial fluid (also known as "extracellular fluid" and includes fluid found in spaces between cells, including, but not limited to, crevicular gingival fluid), bone marrow, cerebrospinal fluid (CSF), saliva, mucous, phlegm, sweat, urine, or any other secretion, excretion, or other body fluids.
    The term "similarity" is a concept related to "identity", but in contrast it refers to a measure of similarity that includes both identical combinations and conservative substitution combinations. Since conservative substitutions apply to polypeptides and not nucleic acid molecules, similarity deals only with polypeptide sequence comparisons. If two polypeptide sequences have, for example, 10 of 20 identical amino acids, and the rest are all non-conservative substitutions, then the percentage of identity and similarity would both be 50%. If, in the same example, there are 5 more positions in which there are conservative substitutions, then the identity percentage remains at 50%, but the similarity percentage would be 75% (15 out of 20). Therefore, in cases where there are conservative substitutions, the degree of similarity between two polypeptide sequences will be greater than the percentage of identity between these two sequences.
    The term "conservative amino acid substitution" refers to a substitution of a native amino acid residue with a normative residue so that there is little or no effect on the polarity or charge of the amino acid residue at that position. For example, a conservative substitution results from displacing a non-polar residue in a polypeptide with any other non-polar residue. Furthermore, any native residue in the polypeptide can also be replaced with alanine. The general rules for conservative amino acid substitution are presented in the table below in this document:

    Conservative amino acid substitution also encompasses unnaturally occurring amino acid residues that are typically incorporated by chemical peptide synthesis rather than synthesis in biological systems. These include peptidomimetics, and other reverse or inverted forms of chemical amino acid portions. Conservative modifications to the amino acid sequence (and the corresponding modifications to the encoding nucleotides) are expected to produce polypeptides that have similar functional and chemical characteristics to those of the biomarkers in Table 1. In contrast, substantial changes in functional and / or chemical characteristics of the biomarkers in Table 1 can be concluded by selecting substitutions that differ significantly in their effect in maintaining (a) the molecular chain structure in the substitution area, for example, as a helical lamina or conformation, (b) the hydrophobic load or capacity of the molecule at the target site, or (c) the volume of the side chain. Naturally occurring residues can be divided into groups based on common side-chain properties: 1) hydrophobic: norleucine, Met, Ala, Vai, Leu, Ile; 2) neutral hydrophilic: Cys, Ser, Thr; 3) acidic: Asp, Glu; 4) basic: Asn, Gin, His, Lys, Arg; 5) residues that influence chain orientation: Gly, Pro; and 6) aromatic: Trp, Tyr, Phe.
    The preferred methods for determining identity and / or similarity are designed to provide the greatest combination of the tested sequences. The methods for determining identity and similarity are encoded in publicly available computer programs. Preferred computer program methods for determining identity and similarity between two sequences include the GCG program package, which includes GAP (Devereux et al., Nuc. Acids Res. 12: 387 (1984); Genetics Computer Group, University of Wisconsin , Madison, Wis.), BLASTP, BLASTN, and FASTA (Atschul et al., J. Mol. Biol. 215: 403 to 10 (1990)). The BLAST X program is publicly available from the Centro
    National Biotechnology Information (NCBI) and other sources (Altschul et al., BLAST Manual (NCB NLM NIH, Bethesda, Md.); Altschul et al., 1990, supra). The well-known Smith Waterman algorithm can also be used to determine identity.
    A "patient" in the context of the present invention is preferably a mammal. The mammal can be a human, non-human primate, mouse, rat, dog, cat, horse or cow. A patient can be one who has previously been diagnosed or identified as having Diabetes, pre-Diabetes, or a condition related to Diabetes, and optionally has already undergone, or undergoes a therapeutic intervention of Diabetes, pre-Diabetes, or condition related to Diabetes . Alternatively, a patient 15 may also be one who has not been previously diagnosed as having pre-Diabetes, Diabetes and / or a condition related to Diabetes. For example, a patient may be one who exhibits one or more risk factors for pre-Diabetes, Diabetes and / or a condition related to Diabetes, 20 or a patient who does not exhibit any risk factors or a patient who is asymptomatic for pre -Diabetes, Diabetes and / or a condition related to Diabetes. A patient can also be one who is suffering or is at risk of developing pre-Diabetes, Diabetes and / or a condition 25 related to Diabetes. Diagnostics and Prognostics
    The invention provides improved diagnosis and prognosis of pre-Diabetes, Diabetes or a condition related to Diabetes. The risk of developing pre-Diabetes, 30 Diabetes or a condition related to Diabetes can be analyzed by measuring one or more of the biomarkers described in this document, and comparing the measured values to reference or index values. Such a comparison can be made with algorithms or mathematical formula in order to combine information from the results of multiple individual biomarkers and other parameters into a single measurement or index. Patients identified as having a higher risk of pre-Diabetes, Diabetes or a condition related to Diabetes can optionally be selected to receive treatment regimens, such as administration of prophylactic or therapeutic compounds or implantation of exercise regimens or dietary supplements to avoid, treat or postpone a principle of pre-Diabetes, Diabetes or a condition related to Diabetes.
    The quantity of the biomarker can be measured in a test sample and compared to a reference or normal level, which uses techniques such as reference limits, discrimination limits, or risk definition thresholds to define cut-off points and abnormal values of pre-Diabetes, Diabetes or a condition related to Diabetes. The normal level of control is the level of one or more biomarkers or combined biomarker indices typically found in a patient who does not suffer from pre-Diabetes, Diabetes or a condition related to Diabetes. The normal and abnormal levels and cut-off points can vary based on whether a biomarker is used alone or on a formula that combines with other biomarkers in an index. Alternatively, the normal or abnormal level may be a database of biomarker patterns or "signatures" from previously tested patients who developed or did not develop or converted to pre-Diabetes, Diabetes or a condition related to Diabetes over a time horizon clinically relevant.
    The present invention can be used to create continuous or categorical measurements of the risk of developing or converting to pre-Diabetes, Diabetes or a condition related to Diabetes, which diagnose and thereby define the risk spectrum of a category of patients with a clinical condition defined. In the categorical setting, the methods of the present invention can be used to distinguish between normal cohorts and cohort with pre-Diabetes, Diabetes or a condition related to Diabetes. In other embodiments, the present invention can be used in order to distinguish pre-Diabetes from Diabetes, Diabetes from normal, different conditions related to Diabetes or diabetes conditions other than normal. Such differentiated use may require different combinations of marker on individual panels, mathematical algorithms, and / or cutoff points, but subject to the same measurement measurements mentioned above for the intended use.
    Identifying a patient before he develops pre-Diabetes, Diabetes or a condition related to Diabetes allows the selection and initiation of various therapeutic interventions or treatment regimes in order to postpone, reduce or prevent the conversion of the patient to a disease state . Monitoring the levels of at least one biomarker also allows the course of treatment of pre-Diabetes, Diabetes or a condition related to Diabetes to be monitored. For example, a sample can be provided from a patient undergoing treatment regimens or therapeutic interventions, for example, drug treatment, for pre-Diabetes, Diabetes or a condition related to Diabetes. Such treatment regimens or therapeutic interventions may include exercise regimens, dietary modification, dietary supplementation, bariatric surgical intervention, administration of pharmaceutical products, and treatment with therapeutic or prophylactic products used in patients diagnosed or identified with pre-Diabetes, Diabetes or a condition related to Diabetes. Samples can be obtained from the patient at various points in time before, during, or after treatment.
    The present invention can also be used to screen populations in a variety of configurations. For groups of patients they can be screened: to identify those required interventions; for the collection of epidemiological data; to review them for health insurance purposes. Data obtained from population screenings will be particularly valuable when correlated with clinical measurements of pre-Diabetes, Diabetes or a condition related to Diabetes and can be stored in data layouts or other collections in a machine-readable medium for convenient use by healthcare providers. health care service and the allied health industry to improve service delivery and efficiency and, therefore, improve patient outcomes.
    A machine-readable storage medium includes any data storage material encoded with computer-readable data or data arrangements that, when using a machine programmed with instructions for using said data, are capable of being used for a variety of purposes, such as how, without limitation, providing or generating patient information related to pre-Diabetes, Diabetes or a Diabetes-related condition risk factors periodically or in response to interventions or therapies and drug discovery. The analysis or measurement of the biomarkers of the invention and / or the corresponding risk determined from it can be implemented by running computer programs on programmable computers, which include, among others, a processor, a data storage system (which includes volatile and non-volatile memory and / or storage elements), at least one input device and at least one output device. The program or software code can be applied to release data, to perform the functions required to generate the required emission.
    The program or software code can perform one or more of the functions in relation to the data related to the biomarkers which includes: determining the normal and abnormal levels of a biomarker and comparing a level of a biomarker to a reference value, for example, a control patient or population whose pre-Diabetes, Diabetes, or Diabetes-related condition is known or an index value or baseline value. The reference sample or index value or baseline value can be taken or derived from one or more patients who have been exposed to a treatment, or it can be taken or derived from one or more patients who are at low risk of developing pre -Diabetes, Diabetes or a condition related to Diabetes, or can be taken or derived from patients who have shown improvements in one or more risk factors associated with pre-Diabetes, Diabetes or a condition related to
    Diabetes (which includes established clinical parameters) as a result of exposure to treatment. The reference sample or index value or baseline value can also be taken or derived from one or more patients who have not been exposed to treatment. For example, samples can be collected from patients who received initial treatment for pre-Diabetes, Diabetes or a condition related to Diabetes 25 and subsequent treatment for pre-Diabetes, Diabetes or a condition related to Diabetes to monitor the progress of treatment. A reference value can also comprise a value derived from a risk forecasting algorithm or indexes computed from population studies.
    The biomarkers of the present invention can therefore be used to generate a biomarker profile or signature from patients: (i) who do not and are not expected to develop pre-Diabetes, Diabetes or a condition related to Diabetes and / or (ii) who have or are expected to develop pre-Diabetes, Diabetes or a condition related to Diabetes. A patient's biomarker profile can be compared to a predetermined or reference biomarker profile to diagnose or identify patients at risk of developing pre-Diabetes, Diabetes or a Diabetes-related condition, to monitor disease progression, as well as the rate of disease progression, and to monitor the effectiveness of treatments for pre-10 Diabetes, Diabetes or a condition related to Diabetes. The biomarker profiles of the present invention are preferably contained in a machine-readable medium and are "live" insofar as they can be updated with additional data that is presented, thereby improving the strength and clinical significance of the biomarkers. The data in relation to the biomarkers of the present invention can also be combined or correlated with other data or test results, such as, without limitation, measurements of clinical parameters or other algorithms for pre-Diabetes, 20 Diabetes or a condition related to Diabetes. Other data include age, ethnicity, body mass index (BMI), total cholesterol levels, blood glucose levels, blood pressure, LDL and HDL levels. Machine-readable media can also comprise patient information such as medical history and any relevant family history.
    The present invention also provides methods for identifying agents to treat pre-Diabetes, Diabetes or a Diabetes related condition that are appropriate or otherwise customized for a specific patient. In this regard, a test sample from a patient, exposed to a therapeutic agent or a drug, can be taken and the level of one or more biomarkers can be determined. The level of one or more biomarkers can be compared to a sample derived from the patient before and after treatment, or it can be compared to samples derived from one or more patients that showed improvements in risk factors as a result of such treatment or exposure. Tests
    The biomarkers and panels of the same invention can be implanted in a range of test systems. Typically, test systems include a means to obtain test remains from a sample, a means to collect, store, process and / or track test results for the sample, usually in a database and a means to report test results. The means of obtaining the test results may include a module adapted for automatic testing that uses one or more among 15 biochemical, immunological and nucleic acid detection tests.
    Some test systems can process multiple samples and can run multiple tests on a given sample. The means for collecting, storing, processing and / or tracking test results may comprise a storage device for physical and / or electronic data, such as a hard disk or flash memory or paper prints. The means for reporting test results may include a visible display, a link to a data structure or database, or a printer. In this regard, the reporting medium may simply be a data link that is adapted to send results to another device such as a database, visual display, or printer.
    Thus, the present invention provides a test system adapted to assist in the identification of 30 individuals at risk of developing pre-Diabetes, Diabetes or a condition related to Diabetes or diagnosed pre-Diabetes, Diabetes or a condition related to Diabetes, being that the test system comprises a medium that uses data that refer to at least one of the biomarkers described in this document. Typically, the test results of the system of the present invention serve as inputs to a computer or microprocessor programmed with a machine or software code that takes data relating to at least one of the biomarkers described in this document and determines the risk of developing or already have pre-Diabetes, Diabetes or a condition related to Diabetes. Biomarker Selection
    The biomarkers in Table 1 were identified as being found to have altered or modified the levels of presence or concentration in patients who have Diabetes and or diabetic nephropathy. In this way, the biomarkers and methods of the present invention allow someone skilled in the art to identify, diagnose, or otherwise analyze patients who do not exhibit any symptoms of pre-Diabetes, Diabetes or a condition related to Diabetes, but who nevertheless have or are at risk of developing pre-Diabetes, Diabetes or a condition related to Diabetes.
    One or more of the biomarkers in Table 1 or 2 can be selected to form a panel of markers. For example, one embodiment of the invention is a method of assessing the risk of developing pre-Diabetes, Diabetes or a condition related to Diabetes, which comprises the step of measuring levels of at least 2, 3, 4, 5, 6, 7 , 8, 9, 10, 11, 12 or 13 biomarkers from Table 1 or 2. Preferably, the panel includes at least one of: Peroxiredoxin-2 (P32119), AMBP Protein (P02760); Apolipoprotein UM-IV (P06727) and the Complement Clq sub-component of subcomponent B (P02746); at least one of Adiponectin (Q15848), complement factor H-related protein 2 (P36980), Hemoglobin beta Subunit (P68871), Apolipoprotein B-100 (P04114) and Sulfhydryl Oxidase 1 (000391) or; at least one of Apolipoprotein C-III (P02656), insulin-like growth factor-binding protein 3 (P17936), antigen-like CD5 (043866) and beta-chain complement component C8 (P07358) Clinical algorithms
    The results obtained using the biomarkers of the present invention can be combined into indices useful in the practice of the invention using any one or more formulas. As indicated above, and without limitation, such indices may indicate, among various other indications, the likelihood, similarity, absolute or relative risk, time or rate of conversion from one disease state to another, or create predictions of future measurements of biomarkers of pre-Diabetes, Diabetes or a condition related to Diabetes. This can be, for a specific period or time horizon, or for the risk of remaining life span, or simply be provided as an index relative to the other reference patient population.
    Preferred formulas include the broad class of statistical classification algorithms, such as relative operation characteristic (ROC), the use of discriminant analysis, for example, linear discriminant analysis (LDA). The attributes can be identified for the LDA using an eigen-based approach with different thresholds (ELDA) or a scaling algorithm based on a multivariate analysis of variance (MANOVA). The forward, reverse and scheduling algorithms can be performed in a way that minimizes the probability of non-separation based on the Hotelling-Lawley statistic. Other formulas include a support vector machine (SVM), a random forest or recursive partitioning can also be used separately or in combination to identify the biomarker combinations that are most important.
    Another formula can be used to pre-process the results of individual biomarker measurements into more valuable forms of information, prior to further processing. 0 Pre-processing includes square root and inverse transformations, normalization of biomarker results, uses mathematical transformations such as logarithmic or logistic functions. Normalizations based on clinical parameters such as age, gender, race, BMI or sex are particularly preferred.
    One or more clinical parameters can be used in the practice of the invention in combination with the biomarkers of the present invention as an entry to a formula or as 15 pre-selection criteria that define a relevant population to be measured using a biomarker panel particular and formula. Clinical parameters can also be useful in the normalization and pre-processing of the biomarker, or in the Biomarker Selection, panel building, formula type selection and derivation, and post-processing of the formula result.
    The biomarker panels of the present invention can be adjusted to the population and outcome or the intended use. For example, biomarker panels and formulas 25 can be used for patient analysis for primary prevention and diagnosis and for secondary prevention and management. For primary analysis, the panels and formulas can be used to predict and stratify risk for conditions, for the diagnosis of diabetic conditions, for the prognosis of glucose level and rate of change and for indication of future diagnosis. For secondary prevention and management, panels and formulas can be used for prognosis and risk stratification for Diabetes complications. Panels and formulas can be used to support clinical decision making, such as determining both to differentiate the intervention at the next visit, to recommend normal preventive checks, to recommend a higher frequency of visit, to recommend improved testing and to recommend therapeutic intervention. Panels and formulas can also be useful for intervention in patients with diabetic conditions, such as therapeutic selections and response, adjustment and dosage of therapy, monitoring efficiency of therapeutic progress and indication for changing therapeutic intervention.
    The disease outcomes of the invention include pre-Diabetes, Diabetes or a condition related to Diabetes. The panels and formulas in this document can be used to assess the current status of disease outcomes assisting the diagnosis and / or determining the severity of pre-Diabetes, Diabetes or a condition related to Diabetes and / or determining the subclass of the disease or disease. The panels and formulas in this document are also useful for determining the future status of the intervention, such as determining the prognosis of future pre-Diabetes, Diabetes or a condition related to Diabetes with therapy, intervention and drug therapy. The invention can be adjusted to a specific intervention, drug class, therapeutic class or therapy or drug therapy or a combination thereof.
    Substitute outcomes of the invention include measuring HBAIc, glucose (I7PG and OGTT), and glucose class (normal glucose tolerance (NGT), IGT, IFG and T2DM). The panels and formulas in this document are useful for determining the current status of surrogate outcomes by diagnosing the glucose class with or without fasting. The future status of substitute outcomes can be determined using the biomarker panels in this document as well as determining the prognosis of a future glucose class. The biomarker panels and formulas are also useful for determining the future state of intervention, such as determining the prognosis of a future glucose class with drug therapy.
    The complication outcomes of diabetic conditions include the conditions related to Diabetes in this document, such as kidney disease, eye retinopathy, microvascular damage, liver damage, limb amputation and cardiovascular complications. The biomarker panels and formulas can be used to assess the current status of disease outcomes to assist in the diagnosis of pre-Diabetes, Diabetes or a condition related to Diabetes. The future state of complication outcomes can be determined with the use of biomarker panels and formulas, such as determining the prognosis of future pre-Diabetes, Diabetes or a condition related to Diabetes. Panels and formulas are also useful for determining the future state of intervention such as determining the prognosis of future pre-Diabetes, Diabetes or a condition related to Diabetes with therapy. Agents to treat or reduce the risk of developing pre-Diabetes, Diabetes or a condition related to Diabetes
    The biomarkers of the present invention can also be used to identify and analyze agents to treat or reduce the risk of developing pre-Diabetes, Diabetes or a condition related to Diabetes. Thus, the present invention also provides a method of identifying or analyzing an agent to treat or reduce the rich of developing pre-Diabetes, Diabetes and / or a condition related to Diabetes which comprises: (i) contacting cells that express at least one biomarker from Table 1 or 2 with a putative agent; and (ii) comparing the expression or level of at least one biomarker from Table 1 or 2 in the cells before contacting the putative agent for the expression of at least one biomarker from Table 1 or 2 in the cells after contact with the putative agent ; where a change in expression or level identifies the agent as an agent to treat pre-Diabetes, Diabetes and / or a condition related to Diabetes.
    The cells can be contacted with the putative agent in vivo, such as in an animal model or in vitro, such as in a cell line or culture. The expression or level can be compared with the use of a software or computer-driven program.
    The present invention also provides a method for treating or reducing the risk of developing pre-Diabetes, Diabetes and / or a Diabetes-related condition in an individual comprising administering an effective amount of an agent adapted to alter the expression or level of at least a biomarker in Table 1 or 2 to the individual.
    The agent can be administered according to any of the known methods as selected by a qualified professional. The agents can be administered as part of a composition that comprises an effective amount of the agent in the mixture with a pharmaceutically acceptable agent such as a pharmaceutically acceptable carrier. The carrier material can be water for injection, preferably supplemented with other common materials in solutions for administration to mammals. Pharmaceutically acceptable agents such as carriers, diluents and excipients can be included as desired. Other exemplary compositions comprise a Tris buffer of about pH 7.0 to 8.5 or acetate buffer of about pH 4.0 to 5.5 which can additionally include sorbitol or a suitable substitute thereof.
    The optimal formulation of the agent will be determined by one skilled in the art depending on the intended route of administration, delivery format and desired dosage. See, for example, Remington's Pharmaceutical Sciences, 1435 to 1712 (18th Edition, A. R. Gennaro, ed., Mack Publishing Company 1990). Such compositions can influence physical state, stability, in vivo release rate and in vivo removal rate.
    Thus, the present invention also provides a method for using an agent adapted to alter the expression or level of at least one biomarker in Table 1 or 2 to prepare a drug to treat or reduce the risk of developing pre-Diabetes, Diabetes and / or a condition related to Diabetes.
    Preferably, the agent adapted to alter the expression or level of at least one biomarker in Table 1 or 2 is an agent to treat or reduce the risk of developing pre-Diabetes, Diabetes and / or a condition related to Diabetes as defined in this document Other agents to treat or reduce the risk of developing pre-Diabetes, Diabetes and / or Diabetes-related conditions include lipase inhibitors such as cetilistat; synthetic amylin analogs such as Symlin pramlintide with or without recombinant leptin; sodium-glucose 2 cotransporter inhibitors with sergliflozin, YM543, dapagliflozin, triglyceride lipid double adipose and PI3 kinase activators such as Adyvia; neuropeptide Y2, Y4 and Y5 receptor antagonists, synthetic analog of human hormones PYY3-36 and pancreatic polypeptide; cannabinoid CB1 receptor antagonists such as rimonabant, taranabant, CP-945,598, hormones such as oleoylestrone; serotonin, dopamine and norepinephrine inhibitors (also known in the art as "triple monoamine reabsorption inhibitors") as tesofensin; norepinephrine and dopamine reabsorption inhibitors, such as Contrave (bupropion plus naltrexone opioid antagonist) and Excalia (bupropion plus anticonvulsant zonisamide); 111.beta.-hydroxysteroid dehydrogenase type 1 (11b-HSD1) inhibitors; cortisol synthesis inhibitors such as ketoconazole; gluconeogenesis inhibitors; glucokinase activators; antisense protein tyrosine phosphatase-1B inhibitors; as well as other agents such as gastrin injections and epidermal growth factor (EGF) analogs such as Islet Neogenesis Therapy (El-I.N.T.); and betahistine. Biomarker Measurement
    Biomarkers can be measured using one or more of a range of techniques. Preferably, biomarkers are measured in a way that minimizes individual variability. For example, they can be measured in a fasted state and, more commonly, in the morning providing a reduced level of individual variability due to both food consumption and metabolism and daytime variation. Any sampling procedure based on fasting or time can be used in the present invention.
    The actual measurement of the levels of the biomarkers in this document can be determined at the protein or nucleic acid level using any method known in the art. For example, at the nucleic acid level, Northern and Southern hybridization analysis, as well as ribonuclease protection assays using probes that specifically recognize one or more of these sequences can be used to determine gene expression. Biomarker levels can also be measured using PCR assays based on reverse transcription (RT-PCR), for example, using specific primers for the differently expressed gene sequence. Preferably, the biomarker levels are determined at the protein level, for example, by measuring the levels of peptides encoded by the gene products described in the present document or activities thereof. Such methods include, for example, antibody-based immunoassays for proteins encoded by genes, aptamers or molecular imprints.
    The biomarkers in Table 1 or 2, polypeptides, peptides, mutations and polymorphisms thereof can be detected in any suitable manner, but are typically detected by contacting a sample of the individual with an antibody that binds to the protein, polypeptide, mutation or polymorphism of biomarker and then detecting the presence or absence of a reaction product. The antibodies can be monoclonal, polyclonal, chimeric or a fragment of the above and the step of detecting the reaction product can be performed with any suitable immunoassay.
    Immunoassays performed in accordance with the present invention can be homogeneous assays or heterogeneous assays. In a homogeneous assay, the immunological reaction usually involves the antibody specific for the biomarker, a labeled analyte and the sample of interest. The signal that appears on the label is modified, directly or indirectly, by binding the antibody to the labeled analyte. Both the detection and the immunological reaction of its extension can be performed in a homogeneous solution. The immunochemical labels that can be employed include free radicals, residioisotopes, fluorescent dyes, enzymes, bacteriophages or coenzymes. reagents are usually the sample, the antibody and the means to produce a detectable signal. The samples as described above can be used. The antibody can be immobilized on a support such as a microsphere (such as protein A and protein G agarose microspheres), plate or slide and contacted with the specimen suspected of containing the antigen in a liquid phase. The support is then separated from the liquid phase and either the support phase or the liquid phase is examined for a detectable signal that employs means to produce such a signal. The signal is related to the presence of the analyte in the sample. Means for producing a detectable signal include the use of radioactive tags, fluorescent tags or enzyme tags. For example, if the antigen to be detected contains a second binding site, an antibody that binds to that site can be conjugated to a detectable group and added to the liquid phase reaction solution before the separation step. The presence of the detectable group on the solid support indicates the presence of the antigen in the test sample. Examples of suitable immunoassays include oligonucleotides, immunoblotting transfer, immunoprecipitation, immunofluorescence methods, chemiluminescence, electrochemiluminescence (ECL) or enzyme linked immunoassays.
    Using the sequence information provided by the database entries for the biomarkers in Table 1, the expression of the biomarker sequences can be detected '(if present) and measured using techniques well known to a person skilled in the art such as analyzes Northern blot hybridization methods or methods that amplify specifically and preferably quantitatively the nucleic acid sequences. As another example, the sequences can be used to construct primers to specifically amplify the biomarker sequences in, for example, detection methods based on amplification such as polymerase chain reaction based on reverse transcription (RT-PCR). When changes in gene expression are associated with gene amplification, deletion and mutations, sequence comparisons in reference and test populations can be made by comparing the relative amounts of the DNA or RNA sequences examined in the reference cell populations and test.
    The biomarker acid and / or protein metabolites can also be measured using one or more of a variety of methods known to one skilled in the art, including refractive index (IR) spectroscopy, ultraviolet (UV) spectroscopy, analysis fluorescence, radiochemical analysis, near infrared (near IR) spectroscopy, nuclear magnetic resonance (NMR) spectroscopy, light scattering analysis (LS), mass spectroscopy including multiple reaction monitoring mass spectroscopy (MRM), pyrolysis mass, nephelometry, dispersive Raman spectroscopy, gas chromatography combined with mass spectroscopy, liquid chromatography combined with mass spectroscopy, flight time and matrix assisted laser desorption ionization (MALDI-TOF) combined with mass spectroscopy, ion spray spectroscopy combined with mass spectroscopy, capillary electrophoresis, NMR and IR detection.
    When biomarkers are measured using mass spectroscopy, they can be measured using a peptide selected from the list of: (i) a peptide of 5 to 25 amino acids from a protein in Table 1 or 2; (ii) a 5 to 20 amino acid peptide from a protein in Table 1 or 2; (iii) a 10 to 20 amino acid peptide from a protein in Table 1 or 2; (iv) a peptide of 10 to 15 amino acids from a protein of Table 1 or 2; or (v) a peptide in Table 3. Kits
    The invention also includes a biomarker detection reagent, for example, an antibody specific for a biomarker protein in Table 1 or 2 or peptide in Table 3 or a nucleic acid that specifically identifies or binds to one or more nucleic acids that encode a biomarker protein in Table 1 or 2 or a peptide in Table 3 having homologous nucleic acid sequences, such as oligonucleotide © sequences or aptamers, complementary to a portion of the nucleic acid packaged together in the form of a kit. The kit may contain in separate containers a nucleic acid or antibody (either attached to a solid matrix or packaged separately with reagents to bind them to the matrix), control formulations (positive and / or negative) and / or a detectable label such as fluorescein, green fluorescent protein, rhodamine, cyanine dyes, Alexa dyes, luciferase, radio tags, among others. Instructions for running the assay can also be included in the kit. The assay can, for example, be in the form of a Northern hybridization, sandwich ELISA or protein antibody array.
    The reagents for detecting biomarkers of the present invention can be immobilized on a solid matrix such as a porous strip to form at least one biomarker detection site. The measurement or detection region of the porous strip can include a plurality of sites that contain an antibody or nucleic acid. A test strip can also contain sites for negative and / or positive controls.
    Alternatively, the control sites can be located on a strip separate from the test strip. Optionally, the different detection sites can contain different amounts of immobilized antibodies or nucleic acids, for example, a larger amount at the first detection site and smaller amounts at subsequent sites. Upon adding the test sample, the number of sites that exhibit a detectable signal provides a quantitative indication of the amount of biomarker present in the sample. The detection sites can be configured in any suitably detectable format and are typically in the shape of a bar or dot across the width of a test strip.
    Alternatively, the kit contains a nucleic acid substrate arrangement that comprises one or more nucleic acid sequences. The nucleic acids in the array specifically identify one or more nucleic acid sequences adapted to link a nucleic acid sequence that encodes a biomarker in Table 1 or 2. The substrate arrangement can be on, for example, a solid substrate or "fragment" . Alternatively, the substrate arrangement can be a solid arrangement. EXAMPLES
    Example 1 - Identification and Validation of Diabetes Biomarkers 1. Materials / Methods A. Cohort description A.l. Fremantle Diabetes Study (Phase 1)
    Background: The FDS1 cohort comprised 1294 patients who had type 2 diabetes. Diabetic individuals with and without diabetic nephropathy were selected to generate different phenotypic presentations allowing for the greatest difference in protein expression.
    The Phase I Fremantle Diabetes Study (FDS) was a longitudinal observational study of diabetes care, control, complications and cost in patients in an urban community defined by a stable postal code of 5 120,097 people. When Phase I was conceived in 1991, there was little published data on the natural history of diabetes.
    A.2. Fremantle Diabetes Study (Phase 2)
    Background: The FDS2 cohort recruited diabetics referred by physicians in the Fremantle location and those in the FDS1 cohort database. Diabetic individuals with and without diabetic nephropathy were selected to generate different phenotypic presentations allowing the greatest difference in protein expression.
    Phase II was designed in 2007 to collect 15 improved and extended data to characterize the nature of diabetes in contemporary urban Australia.

    A.3. Busselton Diabetes Study
    Rationale: Expand information on diabetic patients in a rural community. Complement the information obtained from the urban studies FDS1 and FDS2.
    Includes corresponding non-diabetic control subjects.
    The Busselton Health Study is one of the longest running epidemiological research programs in the world. Residents of the city of Busselton, a coastal community in southwestern Western Australia, have been involved in a series of health surveys since 1966. To date, over 16,000 men, women and children of all ages have participated in the surveys and helped to contribute to the understanding of many common diseases and health conditions.

    B. Development of protein biomarker using iTRAQ and 2D LC MALDI TOF / TOF
    This revealed methodology involves chemically labeling the plasma of different groups of patients (for example, diabetic nephropathy versus diabetic without nephropathy) and determining by mass spectrometry the relative ratio of the presence of a particular protein. Proteins with significantly changed concentrations after analysis indicate a change in the biochemistry of one group of patients 10 versus another. This technique was used to measure the relative concentrations of 130 to 200 proteins per sample. Proteins of significantly different concentration between groups were identified and these were selected for further examination by the MRM methodology (section 15 C below). B.l. Sample preparation
    Plasma samples (N = 10 or 20) were pooled prior to immunodelection of the 14 most abundant proteins using a MARS 14 HPLC column (Agilent Technologies). The immunodeletured samples were exchanged for buffer with the use of 10KDa rotation rotation filters (Sartorius) in 1M Triethylammonium bicarbonate. The protein samples were reduced, scaled, digested by trypsin and labeled according to the iTRAQ protocol (Applied Biosystems). 10 B.2. Instrumental analysis
    The peptides were desalted on a 33 pM Strata-X polymeric inverted phase column (Phenomenex) before separation by strong cation exchange liquid chromatography (SCX) on an Agilent 1100 HPLC using a PolySulfoethyl column (4 , 6 x 100 mm, 5 pm, 300 Å). The peptides were eluted with a linear gradient from 0 to 400 mM KC1. The SCX fractions were desalted and loaded on an Ultimate 3000 nano HPLC system (Dionex C18, PepMap 100, 3 pm) and separated with a 10 to 40% gradient of 20 acetonitrile (0.1% formic acid) with staining using a ProBot robotic locator (LC Packings). The resulting spots were analyzed on a TOF / TOF 4800 MALDI analyzer. B.3. Data analysis
    Data analysis was performed using ProteinPilot ™ 2.0.1 software (Applied Biosystems). False disclosure rates were calculated using the PSPEP algorithm that works in conjunction with ProteinPilot ™ 2.0.1 and only proteins with an overall false reveal rate 30 (FDR) of adjustment of <5% were accepted. C. Validation of biomarker candidate using Multiple Reaction Monitoring (MRM)
    Multiple Reaction Monitoring (MRM) is a mass spectrometry-based approach to specifically target transitions (precursor-fragment ion pairs) to a signature peptide, which represents a replacement for the entire biomarker candidate protein. For each candidate, one or two peptides exclusive to this protein (when compared to the SwissProt Human Database version 57.1) were used. This high-throughput approach was used to validate the biomarkers of the development phase (see Section above) on a larger number of individual patient plasma samples. C.l. Sample preparation
    Grouped samples identical to previous iTRAQ experiments as well as individual samples (N = 10 per group) different from previous iTRAQ clusters (validation samples) were prepared. The samples were immunoreduced from the 14 most abundant proteins using a MARS 14 HPLC column. The immunoreduced samples were exchanged for buffer using 10 kDa cut-off filters. Protein samples were reduced, scaled, digested by trypsin and desalted. In addition, a plasma reference sample (grouping of healthy individuals) was labeled by 18O and finally embedded in each cohort sample (1: 1) prior to LC-MRM / MS analysis. C.2. Translation of biomarker lists into MRM transition lists
    The preliminary MRM transition lists were generated by a series of steps that included downloading protein sequences, digesting silicon proteins together with a filter (for example, 7 to 21 amino acids, 0 missing cleavage) and selecting a minimum of 4 peptide transitions (usually precursor charge z2, product charge zl). Useful information about proteotypic peptides from the literature and repositories (PeptideoAtlas, MRMaid) were also incorporated and the selection of transitions was supported by spectral libraries (ISB, NIST, GPM, BiblioSpec). Open software called Skyline (MacCoss laboratory, University of Washington, Seattle, WA, USA) was used to generate and refine MRM transitions as well as to analyze MRM transition data.
    An aliquot of 1 µg of plasma digestion was loaded directly into a nanocolumn (Dionex C18, PepMap 100, 3 pm) and the peptides were eluted with a 100 minute gradient of 2 to 30% acetonitrile (0.1% formic acid) in a 4000 QTrap equipped with a nanoelectrospray ionization source. A maximum of 200 MRMs were acquired per run with a contact time of 20 milliseconds and a cycle of 5 seconds. Executions 15 were analyzed (ie, peptides without reasonable transitions were deleted) and a refined list of peptides and transitions was subjected to an MS / MS experiment triggered by MRM to validate the peptide designation. Since the designation of peptide for low-abundant proteins is quite a challenging task without standards, the definitions of product ion scanning (PPE) varied, for example, the scanning rate (1,000 to 4,000), LIT fill time ( 20 to 300 milliseconds). The two most intense transitions per peptide were selected for validation and 25 were sent to MS / MS (mass range 200 to 1,200) when a transition exceeded a limit of 1,000 cps. In total, 40 MRMs per run were used with a contact time of 20 milliseconds and a cycle of ~ 7 seconds. The acquired MS / MS data were searched against a current 30 SwissProt database with a human taxonomy filter using MASCOT. The identified peptides were compared with the MRM data (peptide sequence, retention time). Finally, the validated peptides were tested for suitability for use with the MRM 18O tagging method. The final transition list for each cohort study consisted of 1 to 2 peptides (see Table 3) per candidate protein (see Table 1) and 3 transitions 5 per peptide. If possible, peptide sequences that were not exclusive to the candidate protein and peptides with amino acids M, W, N-terminal Q or E, etc. were excluded. Table 3

    C.3. Instrumental analysis
    All samples were reconstructed and embedded 1: 1 with a 180-labeled reference plasma (grouping of healthy individuals) prior to LC-5 MRM / MS analysis to correct spray efficiency and ionization differences between runs. Each sample was injected in duplicate directly into a nanocolumn (Dionex C18, PepMap 100, 3 μrn) and the peptides were eluted in a 100-minute gradient from 2 to 30% acetonitrile (0.1% formic acid) in a 4000 QTrap equipped with a nanoelectrospray ionization source. The programmed MRM option was used for all data acquisition with a target scan time of 4 seconds (at least 8 data points over a peak) and a 6 to 8 minute MRM detection window that 10 resulted in minimum contact times of 50 to 60 milliseconds. C.4. Data analysis
    All transitions were integrated and for each peptide a (weighted) ratio of the area of the unlabeled peptide to the area of the tagged peptide was calculated. The reasons have been normalized for population-based differences based on an invariable set of proteins. Finally, a Mann-Whitney test for non-parametric data was applied for normalized ratios and a p-value was calculated that defines a protein as significantly differently expressed between two groups of individuals, for example, healthy versus sick.
    The sensitivity or true positive rate, versus the false positive rate (Relative Operational Characteristic curves) were also plotted for a range of markers (univariate and multivariate). A number of statistical transformations were used to improve strength including the natural log (ln), inverse (inv) and square root (V). 2. Results D. Biomarkers Dl. Biomarkers for diabetic nephropathy in diabetic patients
    The table in Figure 1 shows data from Busselton biomarker protein and both Fremantle Diabetes Studies in relation to the presence of diabetic nephropathy in which all individuals had diabetes. The question that is addressed is' what are the biomarkers for diabetic nephropathy in diabetic patients "
    The results of the table in Figure 1 are illustrated as box and mustache wire plots in Figure 2 (Study FDS1), Figure 3 (Study FDS2) and Figure 4 (Study BDS). For 10 each biomarker candidate one to two signature peptides per protein were measured by MRM. (left box plot: diabetic group; right box plot: diabetic group with severe nephropathy; geometric axis x: protein / peptide; geometric axis y: relative abundance ratio 15).
    The ROC data in Tables 4 to 8 further illustrate that the biomarker (s) can be used as a diagnosis for diabetic nephropathy. Table 4 Univariate analysis
    Table 5 Multivariate analysis (Model 3)
    Table 6 Multivariate analysis (Model FDS1)
    Table 7 Very diverse analysis (Model FDS2)
    Table 8 Multivariate analysis (BDS model)
    D2. Biomarkers for diabetics with nephropathy versus healthy patients
    The table in Figure 5 describes the biomarkers discovered for patients with diabetic nephropathy versus a healthy control group without diabetes. These data are derived from the Busselton study.
    As should be apparent, various changes and equivalent forms can be provided without departing from the spirit or scope of the present invention. This includes 10 modifications within the scope of the appended claims together with all modifications, alternative and equivalent constructions.
    In the present specification, the presence of private resources does not eliminate the existence of additional resources 15. The words "understand", "include" and "have" must be interpreted in an inclusive rather than an exclusive sense.
    权利要求:
    Claims (14)
    [0001]
    1. METHOD IN VITROPARA TO EVALUATE A PATIENT AS TO DIABETIC NEPHROPATHY, characterized by understanding: measuring at least one biomarker in a patient sample, in which said at least one biomarker is CD5 antigen.
    [0002]
    2. METHOD according to claim 1, characterized in that at least one biomarker additionally comprises a biomarker selected from the list of biomarkers comprising: peroxiredoxin-2, AMBP protein, complementary Clq subcomponent B subunit, factorapolipoprotein C-III, protein binding to insulin-like growth factor 3, adiponectin, complement factor H-related protein 2, hemoglobin beta subunit, apolipoprotein B-100, sulfhydryl oxidase 1, beta C8 chain complement component and apolipoprotein A-IV.
    [0003]
    METHOD according to claim 1, characterized in that at least one biomarker comprises a biomarker selected from the group comprising Apolipoprotein A-IV and insulin-like growth factor-binding protein 3.
    [0004]
    4. METHOD according to claim 1, characterized in that at least one biomarker comprises apolipoprotein A-IV and insulin-like growth factor-binding protein 3.
    [0005]
    5. METHOD according to any one of claims 1 to 4, characterized by the step of measuring at least one biomarker in a patient sample comprising detecting a peptide fragment of said at least one biomarker.
    [0006]
    6. METHOD according to claim 5, characterized in that the peptide fragment is a 5-25 amino acid peptide fragment.
    [0007]
    METHOD, according to either of claims 5 or 6, characterized in that the peptide fragment is selected from the group comprising SEQ ID NO's: 1-2.
    [0008]
    METHOD, according to any one of claims 5 to 7, characterized in that the peptide fragment is selected from the group comprising SEQ ID NO'S: 2, 3, 4, 5, 16 and 17.
    [0009]
    Method according to any one of claims 5 to 8, characterized in that said peptide fragment is detected using mass spectrometry.
    [0010]
    10. METHOD, according to any one of claims 1 to 9, characterized by the patient being asymptomatic for or only displaying non-specific indicators of diabetic nephropathy.
    [0011]
    11. METHOD, according to any one of claims 1 to 10, characterized in that the patient has been diagnosed with diabetic nephropathy.
    [0012]
    12. METHOD according to any one of claims 1 to 11, characterized in that the patient has kidney disease.
    [0013]
    13. METHOD according to any one of claims 1 to 12, characterized in that the patient has microalbuminuria, macroalbuminuria or end-stage renal disease.
    [0014]
    Method according to any one of claims 1 to 13, characterized in that the sample comprises a blood sample.
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    法律状态:
    2018-01-16| B07D| Technical examination (opinion) related to article 229 of industrial property law|
    2018-04-03| B06F| Objections, documents and/or translations needed after an examination request according art. 34 industrial property law|
    2018-06-19| B07G| Grant request does not fulfill article 229-c lpi (prior consent of anvisa)|
    2019-07-23| B06U| Preliminary requirement: requests with searches performed by other patent offices: suspension of the patent application procedure|
    2020-04-07| B09A| Decision: intention to grant|
    2020-06-09| B25A| Requested transfer of rights approved|Owner name: PROTEOMICS INTERNATIONAL PTY LTD (AU) |
    2020-06-23| B25G| Requested change of headquarter approved|Owner name: PROTEOMICS INTERNATIONAL PTY LTD (AU) |
    2020-11-03| B16A| Patent or certificate of addition of invention granted|Free format text: PRAZO DE VALIDADE: 20 (VINTE) ANOS CONTADOS A PARTIR DE 20/09/2011, OBSERVADAS AS CONDICOES LEGAIS. |
    优先权:
    申请号 | 申请日 | 专利标题
    AU2010904249|2010-09-21|
    AU2010904249A|AU2010904249A0|2010-09-21|Biomarkers associated with pre-diabetes,diabetes and diabetes related conditions|
    PCT/AU2011/001212|WO2012037603A1|2010-09-21|2011-09-20|Biomarkers associated with pre-diabetes, diabetes and diabetes related conditions|
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