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    METHODS FOR IMPROVING DIABETES MANAGEMENT A method for determining the levels of biomarkers, specifically, advanced glycation 5 end products (AGEs) and oxidation products (Ops) in a biological sample such as a plasma ultrafiltrate, is used to determine a patient's risk and/or rate of developing diabetes related nephropathy. The preferred biomarkers to measure include NE-(l-carboxyethyl-lysine (CEL), methylglyoxyl-derived hydroinidazolone (MGII) and NE--carboxymethyllysine (CML). Also provided herein is a method of diabetic care which includes determining a diabetic patient's 10 risk of developing diabetes related kidney disease and adjusting the patient's treatment regimen to include in addition to glucose lowering agents, additional treatments such as medications that modify the renin-angiotensin system, or specialized diets with low levels of AGEs or oxidative products. Hbalc 0.5 1 1.5 2 2.53 4 5 Figure 3
    公开号:AU2013206252A1
    申请号:U2013206252
    申请日:2013-06-11
    公开日:2014-01-09
    发明作者:Paul J. Beisswenger
    申请人:PREVENTAGE HEALTHCARE LLC；
    IPC主号:G01N33-50
    专利说明:
    METHODS FOR IMPROVING DIABETES MANAGEMENT FIELD OF THE INVENTION The invention encompasses methods for monitoring or determining the risk of diabetic 5 nephropathy or an associated disorder in a subject and improving diabetes management. CROSS-REFERENCE TO RELATED APPLICATIONS This application climIs priority to U.SN. U.SS.N. 61/658;218 "Methodsfor hnproving Diabetes Management b Paul J. Beisswenger. BACKGROUND OF THE INVENTION 10 Diabetes mellitus (DM affects more than 25.8 million people in the United States alone, ie. 8 3% of the population. About 1.9 million people aged 20 years or older were newly diagnosed with diabetes in 2010. An estimated 79 million people aged 20 years or older are believed to have prediabetes, which constitutes 5% of adults aged 20 years or older and 50% of adults aged 65 years or older. National Diabetes Information Clearinghouse, 15 National Diabetes Statistics, 2011. Much of the morbidity and cost of diabetes management is attributable to long-term diabetes-related complitations. For example, diabetes is the leading cause of kidney failure, non-traumatic lower limb amputations and new cases of blindness among adults. Diabetes is also a major cause of heart disease and stroke. After adjusting for population age and Nex 20 differences, average medical expenditures among people with diagnosed diabetes vere 2.3 times higher than the expected expenditures without diabetes. The cost of diabetes in 2002 was $175 billion, which includes $1 16 billion in excess medical expenditures and $58 billion in reduced national productivity. Dall, et al, Diabe Care 31(3):596-615 (2008) Based on the current incidence of diabetes and denographics, it has been projected that 25 the number of Americans with diabetic retinopathy will triple to 16 million by 2050, and the ityor cause of the dramatic expansion in rates of end stage renal disease in this country is due to new cases of diabetic nephropathy, People with diabetes alo have a dramatic increase in the risk of heart attack and stroke. It was primarily treated of these devastating complications that drove the cos t of caring for diabetes to $245 billion in 2012, a 45% increase since 2007. 30 The chronic elevation of blood glucose level associated with DM leads to damage of 2 blood vessels. The resulting problems are grouped under'-nicrovascular disease" (due to damage to small blood vessels) and "macrovascular disease" (due to damage to the arteries). The damage to small blood vessels leads to a microangiopathy, which can cause diabetic retinopathy and/or diabetic nephropathy,. Microvascular complications including retinopathy 5 and nephropathy account for the most prevalent and severe morbidity associated with diabetes and are involved in mediating the increased risk of cardio- and cerebrovascular disease as well. Diabetes is also the leading cause of renal insufficiency and end-stage renal disease (ESRD) in the U.S.. and the Westen world. Although diabetic microvascular complications are clearly associated, with the degree of hyperglycemia, not all diabetic individuals with poor 10 glycemic control develop renal or advanced retinal complications. Conversely, some diabetic patients develop severe complications despite well-controlled blood glucose concentrations. It is distressing that there have been virtually no new biornarkers identified for the early detection of diabetic complications over the past 20-30 years, and the current biomarkers for identifying this "high-risk" subgroup continue to have significant limitations A first sign for 15 kidney damage is the presence of protein in urine (micro- or macroalbuninuria) which can be assessed by a clinical laboratory test or the latter with a simple dip stick test The most common test used to date is still serum creatinine while acknowledging its missing accuracy. A limitation of tests relying on miciroalbuminuria, which occurs when kidney damage is already in place, is that it is only useful for detecting diabetic nephropathy at the 20 asymptomatic stage. Early diagnostic or predictive tests would revolutionize diabetes management, because treatment strategies could be set in place to prevent or delay eventual diabetic nephropathy. U. S, Patent No. 6,323,038 discloses a pyridinium compound as a diagnostic reagent for detecting complications associated with diabetes or renal failure. US. Publication No. 25 2011/0079077 discloses urine and serum proteins and their fragments, which alone, or in combination, can be used to diagnose early stage diabetic nephropathy. The current bionarkers (i.e. measures of hyperglycenia) for identifying this "high-risk" subgroup have significant limiitaions. The Diabetes Control and Complications Trial (DCCT) showed that HbA;, (Al C) alone (i.e, levels of glycated hemoglobin) does not completely determine risk 30 of outcomes, (Beisswenger, et al, Diabetes,54 3274-328 1 (2005)) The "Natural History of 3 Diabetic Nephropathy Study" has shown that only 9% of the risk of progressive glornerular basement membrane (GBM) thickening in type 1 diabetes is accounted for by the baseline AlC level. The biomarkers for progression of diabetic retinopathy (DR) and diabetic nephropathy (DN) including retinal morphological change or the appearance of albuminuria 5 on regular exanina tons, are unable to identify those at greatest risk during the long 10-20 year "silent phase" when evolving or incipient damage to the kidney, eyes, and CV system is not clinically apparent (Nathan, et al, New England Journal of Medicine. 353(25) p. 2643-53 (2005)) By the tini.e these markers become positive, substantial pericyte drop-out and avascular capillaries are frequently present in the retina (Ahred, et al, Blochen Soc Trans, 10 3 1(Pt 6):1417 -22 (003), while substantial irreversible kidney damage can be present by the time microabumi nuria occurs (Nathan et al., New England Joural of Medcine,; 353(25):2643-53 (2005)), It is also widely recognized that CV disease may remain silent for many years, in spite of the gradual accumulation of serious and iife-threatening lesions (Mauer, et al. J Renin AngiatensrAidasrerne System. 3:262-269 (2002); Almati, et al., 15 Int. J, of Cardiol, 1091): 715 (2006. In addition, more aggressive treatment for DN with Ace inhibitors (ACEI) and Angiotensin receptor blockers (ARBs) instituted when albuminuria is detected is unable to slow progression of structural glomeruar lesions, as shown by the RASS (Koschinsky. et al., Proc. 4Nat1 A4cad. Sci USA, 94(1.2>6474-6479 (1997)), sugesting that prevention in a highly susceptible individual is a far superior 20 approach. As a result of the inability to adequately predict a diabetic patient's risk of developing diabetes related complications, current clinical treatment decisions are made on. the premise that all diabetic patients are equally susceptible to complications. This approach is limited, however since only 50 % of patients with type 2 diabetes achieve the recommended AIC 25 treatment goal of < 7% in a large population-based study (NHANES) DCCT/EDIC, JAMA, 287(19):2563-9 (2002)), and the success rates are even lower in type I diabetes (Nathan, et al, Diabetes Care, 31(1) 173-5 (2008; Holman, et al- New England Journal of 4V-edicine, 359 15) 1577-89 (2008), Reasons for this large-scale failure include the lack of patient specific predictive information, the overwhelming rates of newly discovered diabetes, the 30 massive expense of providing adequate care, the lack of sufficient and adequately trained 4 medical providers, and patient denial of the potential consequences of poor treatment compliance resulting from their lack of accurate individualized predictive information on risk, Diabetes treatments are not only expensive, but some are accompanied by a high-risk of hypoglycemia and drug side effects, as wel as the expense and risk of new treatments such as 5 pancreatic transplants and the evolving artificial pancreas. Based on these considerations, it will become increasingly difficult to apply these therapies to all patients with diabetes, without having better information on individual risk and benefit. Advanced glycation end products (AGEs) and oxidation products (OPs) have been proposed as possible factors for diabetic complications. Until recently, however, knowledge 10 of these products has been limited to the Early Glycation Products (EGPs), several oxidation end products and a few AGEs. Most prior studies have measured limited numbers of AGEs (Yu e. a]. iabetoogia 49( 10):2488-98 (2006); Monnier et al, Annals of the New York Academy of Sciences, (2008); (Beisswenger, et al, Journal of Clinical Investigation, 92(l)-2 12-7 (1993); Dyer, et al, J. C/in, Invest, 91(6): 2463-9 (1993): Monnier, et al, 15 Annals of theNew York Academy of Sciences, I043:567-581 (2005), particularly pentosidine and carboxyrnethyllysine, or have focused on a few end-products that reflect oxidative stress (Yu, et at Diabetologia 49(10):2488-98 (2006); Baynes, et ak, Free Radical Biology & Medicine> 28(12): 1708-16 (20001 A substantial number of these analyses have also been performed as semi quantitative innunoassays, which have generally not been validated 20 against quantitative chemical analyses. Although sone of these studies have shown correlations between blood levels of these products and complications (Monnier, et al; Annals of the New York Acadenry of Sciences, 1043:567-581 (2005)), none have validated their predictive value in large-scale controlled diabetes outcome studies. A recent study by Perkins, et al. PLoS One, 7(4)335655 (2012) measuring the levels of AGEs and oxidative 25 markers in LC/MS/MS concluded that there was no correlation between any of the protein damage adduct residues of plasma protein nor concentration of related free adduct with subsequent early giomerular filtrate rate (GFR) that leads to end stage renal disease. It is desirable to identify biomarkers that can be used to predict a patients risk of developing diabetes related kidney disease before the patient exhibits known signs and/or 30 markers of kidney disease or malfunction.
    It IS an object of the present invention to provide biomarkers useful for determining a diabetic sub ject's risk of developing kidney disease. It is also an object of the present invention to provide a method for identifying a subject at risk of developing diabetes related kidney disease. 5 It is a further obiect of the present invention to provide a method for identifying a diabetic's risk of eye or cardiovascular disease. SUMMARY OF THE INVENTION A method for determining the levels of bionarkers, specifically, advanced gIycation end products (AGEs) and oxidation products (0Ps) in a biological sample, preferably plasma 10 or plasma ultrafiltrate, or a urine sanple, has been developed. he inethod is useful in detecting the levels of biomarkers such as N carboxv methyl-lysine (CML), N eCaboxy ethyl-I ysi-ne (CEL); 0lyoxa hydroimidazolone (GH )N; Methylglyoxal hydroimi dazolone (MCGH I)3-Deoxyglucosone Hydroimidazolone (3DH19 : methionine sulfoxide (MetSO); and 3-nitrotyrosine (3-NT), dityirsine, and 2-aninoadipic acid. The method of preparation of the 15 samples is important The biomarker levels in the sample are preferably determined by Liquid Chromatography/Tiple Quadrupole Mass Spectroscopy (LC-MSiMS). In one preferred embodiment for measunng binomarkers using LC-MS/MS, the stail onary phase is C.8 with heptafluorobutyric acid being used as an ion pairing agent. This allows the analysis to be performed with a single column relati ve as opposed requiring 2 columns. 20 A method for determining a subjects risk of developing DN or a disorder associated with DN has also been developed. The method includes obtaining a test sample from a subject diagnosed with diabetes, measuring the levels of Nl -carboxyethy-iysine (CEL) methylglyoxyl-derived hydroinidazolone (MGHI) and N~earboxymethyllysine (CML) and comparing the values to the metabolite levels shown to be associated with either progression 25 or non-progression of diabetic nephropathy. Also provided is a method of diabetic care which includes determining a diabetic patient's risk of developing diabetes related kidney disease- and adjusting the patient's treatment reginen to include, in addition to glucose lowering agents, additional treatments such as medications that modify the renin-angiotensin system, or specialized diets with low 6 levels of AGEs or oxidative products, to delay or reduce the severity of kidney, eye or cardiovascular disease. BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 shows the chemical structures of biomarkers, FL = NFructosyllysine; CML 5 Necarboxy methvl-iysine; CEL = Nccarboxy ethyl-lysine; G-HI = glyoxIa hydroimidazolone; MG-HI = Mthylglyoxal hydroirnidazolone; 3DG-H-{ 3-Deoxyglucosone Hydroimidazolone; MetSO methionine sulfoxide 3-NT = 3-nitrotyrosine. Figures 2A-2D. A logistic regression model was used to develop predictive equations relating each biomarker to the probability of a subject's classification as a "fast" progression 10 (F) of DN. CML and MGH I vaues were log transformed when used as predictors and then backtranusforred when creating predictive probability plots. For the 3 biornarkers but not for HbAr (p=0 28) measured at the same time,, there was a significant reladonship to the prohabihity of La ssification as a F P (CML. p=0 02; CEL p=0.03: MGHI p=0.
    048 ). For HIbAl c the relationship was significant when fit to the entire sample (n= 186) over 5 years 15 (p=0.006). Figure 3 is a forest plot of the odds ratios (and 95% confidence intervals) for a one standard deviation change in the three informative biomarkers (CML, CEL, and MGiM), and HbA , as calculated from the logistic regression model. For example, a one SD increase in CEL would lead to a 1,72 increase in the odds of being in the fast progression group The 20 p values for these plots are associated with those shown in Figures 21A-D. DETAILED DESCRIPTION OF THE INVENTION L Definitions 25 "A disorder associated with diabetic nephropathy" as used herein refers to a disorder that stems from angiopathy of capillaries in the kidney glomeruli. Non-limiting examples of associated disorders nay include nephritic syndrome, chronic kidney failure, and end-stage kidney disease. "Diabetic nephropathy" as used herein refers to a disorder characterized by angiopathy 30 of capillaries in the kidney glomeruh. The term encompasses Kimmelstiel-Wilson syndrome, 7 nodular diabetic glomerulosclerosis and intercapillary glomerulonephriti Type I diabetic individuals have a 20-40% risk f developing kidney disease. The bioriarkers described herein allow identification of this populations o that those people who are identified have a 10-50%, or greater risk of developing diabetic nephropathy. 5 IL Method for Predicting a Patient's Risk of Developing Diabetic Nephropathy A. Markers to be Assessed Specific chemical end products have been identified in carefully documented outcome studies based on investigation of the activation of specific glycation and oxidative pathways in clinical populations with documented nephropathv and retinopathy. These pathways lead to the 10 formation of a spectrum of early glycation products (EGPs), incl udin g the chemically reactive a dicarbonyl compounds., methylglyoxal (MG), 3-deoxyglucosone (3DG and glyoxal (G). These a dicarbonyls, in turn, lead the formation of later stage chemical reactions to form advanced glycation end products (AGEs), a process that is independent from HbA, formation. Increased glucose-induced oxidative stress (OS) may also be caused by inherent differences in 15 processes that control cellular oxidative mechanisms, which can directly and independently activate ra jor pathways that produce diabetic complications. It has been found that both dicarbonyl stress and OS are selectively activated in those prone to diabetic complications, resulting in higher levels of glycated and oxidized protein and lipid byproducts, but not directly to changes in HbA, 20 Assays to assess blood and urine levels of selected glycation and oxidative end products of these chemical pathways have been developed to make the measurements more precise. Since these end products represent slowly Luning over end-products of glyeative and oxidative pathways, their use in clinical outcome studies should be superior to measurement of short acting chemical precursors which would not necesarily reflect long-term overproducdon when 25 checked in one or more blood and urine samples. In these studies it was found that the quantitatively highest in vivo AGEs in type 1 diabetes are h droimidazolones (11) derived from argnine residues modified by inethylgiyofxal (MG) 3-deoxyglucosone (3DG), and glyoxal, produced in tumn from their individual synthetic pathways, Other quantifiable AGEs in these studies include glyoxal-iysine derived 30 carboxymethyl (CML), and the MG-iysine product, carboxyethyl-lysine (CEL). Markers of 8 oxidation and nitration, inchiding methionine sulfoxide (MetSO), formed by the oxidation of methionine, 2-aminoadipic acid formed from lysine residues, as well as other markers of nitrosive damage leading to production of 3-nitrotyrosine (3-NT) and dityrosine have also been measured. Substantial differences exist between diabetic and control populations (5 to 15 fold), 5 The unprecedented increases observed in AGEs and OPs in diabetes, and the substantial differences observed between individuals indicate that these biomarker levels, alone or in addition to HbA ic, have the potential to markedly sharpen the ability to differentiate subjects at very high versus very low risk of complications. The key AGEs and Os that are most predictive of complications including hydroimidazolones and MetSO, showed no correlation 10 with HbAI suggesting that they are produced by chemical pathways that relate to complications, but respond uni quely to the level of glycemia based on Individual patient characteristics. AGE and OP biomarkers are measured in plasma and urine samples to assess a person's risk of developing diabetic nephropathy or other complications, By comparing the 15 levels in carefully defined nephropathy progressors and non-progressors, the levels of specific AGE's allow determination of a diabetic subject's risk of developing diabetes related DN or a disorder associated with DN. In a preferred embodiment, plasma filtrate levels of three AGEs, CML = N carboxy methyl-lysine; CEL = Ncarboxy ethyl-lysine; and MG-I-l = Methvglyoxal hydroimidazolone, alone or in combination with HbA1c, are indicators of 20 early progression of DN. In a more preferred embodiment, MG-HI levels alone are an independent predictor of a subject s risk of developing DN. The methods described herein can be used to quantify the following products, which are shown in Figure 1; Arginine derived AGEs: These biomarkers include the quantitatively important 25 hydroimidazolones (H[; which are AGEs derived from arginine residues modified by glyoxal, MG. and 3-DG and include G-Hi (glyoxaI hy droimidazolone), MG-NI (viethyllvoxal hydroimidazolone), and 3DG-H (3-Deoxyglucosone Hydroimidazolone), respectively, Lysine derived AGEs: Other important AGEs that can be measured are lysine-based and include glyoxal derived Nk--carboxvmethl Iysine (CML), and MG delved N carboxyethyl 30 lysine ('CEL) (Thornailey, et aL iuehemical Journal 375(Pt 3:581-92 (2003)) Other AGEs 9 that can be measured include the more traditional product, pentosidine, which is measured by HPLC and fluorescence detection (Sell, et al. Diabetes, 40(Suppi 1):302A (1991) Quantitative markers of oxidative damage to proteins can also be measured and include methionine sulfoxide (MetSO), formed by the oxidation of the sulfhydryl group on 5 Methionine (Yu, et al. Diaberologia, 49(10):2488-98 (2006) The tyrosine cross-link dityrosine, as well as a widely studied marker of combined oxidative/nitration damage to proteins, 3-nitrotyrosine (3-NT), can also be measured (Geibauf, EBPS Letters', 389:136- 140 (1996)). To amplify the inforrnatiion obtained on the role of oxidati ve stress in the development of diabetic complications, another unique oxidative product, 2-aminoadipic acid, 10 a product resulting from metal catalyzed oxidation of lysyl residues (Sell, Biochemical .t, 404(2)1:269-77 (2007)), can also be measured. Urine creatinine levels can also be determined to provide uniform expression of product/creatinine urine analyte content. The urinary and serum "free fraction" determinations allow the calculation of renal clearance rates of each analyze. 15 Studies performed in landmark outcome studies with type 1 diabetes, are applicable to patients with type 2 diabtes as welL It is well recognized that major clinical trials have shown a similar significant relationships between glycemic control and progression of DN and DR in both type I and type 2 diabetes in DCCT and UKPDS fDCCT/ED&CAMLA, 28709:2563-9 (2002: Hohlinn et al- New England Journal of Medicine, 359(15):1577-89 20 (2008), suggesting sinilarities in pathogenesis for both diabetes types. These considerations have led the American Diabetes Association (ADA), American Association of Clinical Endocrinologists (AACE), and European association for the Study of Diabetes (EASD) to recommend similar HbA I guidelines to prevent DR and DN in both type I and 2 diabetes (Nathan, et al., Diabetes Care, 31 (1)-73-5 (2008); Rodhard, et aL. Endocrine Practice. 25 14(6):791-5. (2008).% :B. Sample Collection Methods of collection, storage, and processing of samples are all important, since improper handling can, lead to artifactual sample oxidation. Many AGEs measured in stored 30 plasma samples are stable over multiple years Acceptable stability has been confirmed by 10 observing similar levels of analytes, when we compared levels in plasma samples stored at 80 'Cfor 10-I5 years with those in freshly drawn plasma from diabetic subjects. During sample preparation, it is important to take steps to prevent formation of artefacts, For example, rapid separation of red blood cells front plasma at 4C, use of chelating 5 agents such as EDTA in blood sample, addition of a preservative or antioxidant such as Butylated hydroxytoluene (BHT), immediate snap freezing such as on dry ice, and storage at 80"C are examples of steps that can be taken to minimize formation of artefacts. In the preferred embodiment, plasma samples that had been are collected by a carefully defined protocol, by collecting in EDTA, immediate spinning to sediment red blood cells I0 (RBCs separating plasma from RIBCs at 4 0 C, followed by immediate freezing and long-term storage at -804C. Using this process, MetSO levels were in the expected range when compared with the same samples that showed artifactual OS in serum. None of these modifications were observed in urine samples that were collected and stored by standard protocols. Based on these observations, plasma is a better choice for measurement of OP and AGEs in stored 15 samples since it contains the chelating agent (EDTA) and is immediately spun and separated from RBCs after collection, and flash frozen. Seumn on the other hand, has to undergo clotting at room temperature before separation and. storage, thus exposing proteins to leukocyte myeloperoxidase and other pro-oxidant enzymes. Serum also contains no chelator of trace metals (Fe and Cu), both of which can promote spontaneous in viro ox IdatIve stress. 20 A plasma sample is collected from a subject diagnosed with DM and the levels of one or more of the biomarkers are determined, The preferred sample is a plasma ultrafiltrate. This "free fraction" can be prepared by centrifuagation at 44C through uicrospin filters (10,000 MW filter cut-off, 50 i aliquot). The rational for measuring this fraction is because cells maintain the quality and functional integrity of proteins by degradation and replacement of proteins 25 damaged by oxidation and glycation (Thornalley, et al Biochemica Journal, 375(Pt 3):581-92 (2003) and Goldberg, et al, Nature, 4 6 (696y899(2003)). This occurs by protcolysis liberating the oxidized, glycated, and nitrated, amino acids as free adducts, which in turn are released into blood plasma and excreted in urine (Thornalley, et al, Biochemical Jou'rnal, 375(Pt 3):581-92 (20034 Since these free adducts are released into blood plasma as tissue 30 breakdown of AGEs occurs, changes in plasma concentrations reflects tissue damage in 1 1 diabetes, while providing new markers indicative of the damaging effects of hyperglycemia. Adduct residues chemically react with and become bound to plasma proteins: Since some of the products are acid labile, chemically bound products are determined after exhaustive sequential enzymatic digests using suitable enzymes. Examples of enzymes that 5 can be used include, but are not limited to, pepsin, Pronase E, Aminopeptidase and prolidase. Methods for digesting product chemically bound to plasma proteins are described in Ahmed, et al., Biochemical Journal, 364(Pt 1):1 -14 (2002) The biomarkers in the urine are preferably measured from. a urine filtrate, prepared by centrifugatio at 4C through rmicrospin filters (10,001 MW filter cutoff), Methods for 10 preparing a urine filtrate are described, for example, in Ahmed, et a, Diabewiagia. 48:1590 1603 (20051. Methods for the concurrent quantitative measurement of biomarkers indicative of protein glycauon. oxidation, and nitrosative damage can utilize a system such as an Agilent Model 6410 Triple Quadnipole MS System with 1200 Rapid Resolution LC System The 15 HPLC is performed with a modified prior multicolumn method, by utilizing a single 2.0 X 250 mm Synergy 4micron 80A column (Phenomenex, USA) with a mobile phase of Methanol/H 2 O gradient with 0:29% heptafluorobutyric acid for ion pairing. This methodology has a total run time of 60 min. The method does not have the sensitivity to accurately measure tyrosinie based OPs in plasma filtrate samples 20 A system such as an Agilent Model 6490 Triple Quadnipole MS System with a 1290 Illfinity LC System for Ultra high pressure liquid cbromatography (UHPLC) (Wilmington, Delaware) provides a 1000 fold increase in analytical sensitivity over our previous instrument, and a four-fold improvement in sample throughput. HPLC methodology using ion pairing can be used to resolve the complex mixture of 15 compounds, but it does have several drawbacks. lonpairing agents tend to cause difficulties with HPLC pump performance and constant vigilance and frequent pump washing is needed to keep the system working properly Ion-pairing systems require iong re-equilibration times making it more difficult to design a high throughput environment. Ion--pairing agents may cause ion-suppression and potential loss of signal of target compounds. 30 Another nethodoiogy is hydrophilic interaction chromatography (HILIC), which has 12 recently become an accepted new separation strategy. Like normal phase chromatography the order of elution for compounds is reversed with the more hydrophobic compounds eluting early and the more hydrophilic compounds being retained in the column, providing several advantages including enhanced retention of the hydrophilic compounds that are present in the 5 mix of compounds and unique selectivity that may help resolve some of the previously co~ eluting peaks, Buffers used in this system are ideal for MS/MS detection( low backgrounds and. ion suppression). Changing pH of the system can give an alternate method for modifying the selectivity of the system. The columns are now available in the advanced core-shell technology or UHPLC configurations, which are very amenable to high throughput applications, including 10 Agilent Porous Shell Technologv. Another system is C18 stationary phases, which have been modified for different selectivities and enhanced retention of polar compounds. Modification of buffer pH with some of these alternate systems should lead. to better retention and different compound selectivities. Some examples of this technology include Synergi Polar-RP with an ether linked phenyl group 15 for retention of polar compounds without ion-pairing, or Luna NH 2 phase for polar selectivity and a weak anion exchange capability, which could be utilized at a higher buffer pH to increase retention of Polar compounds. Mixed-mode stationary phases are very new to HPLC, These columns combine C18 and ion exchange capabilities into one column, Many ties the ionwexchan ge capability can mimic 20 the effects of an ion-pairing agent. A mixed mode column with cation exchange capability should also be useful. C. Biomarker Assay and Determination of Risk for Developing DN In a. preferred embodiment, the level of biomarkers in the plasma and urine prepared as discussed above are determined by Liquid Chromatography/Triple Quadrupole Mass 25 Spectroscopy (LC-MS/MS). Utilizing methods involving carefully modified conditions, the biomarkers can be measured utilizing internal standardization by stable isotope substituted standards. To determine the optional sample number required to provide a representative estimate of each of the plasma and urinary biomarkers over time, multiple measurements were used to 30 calculate an anccepable quantitative estimate of each analyte, A representative calculation by 13 this approach follows based on urinary pentosidine levels in 4-6 samples/diabetic subject for ten subjects, over 5 years. For these measurements. it was determined that between person variance '64 x 104 and within person variance = 2,06 x iOt Thus Total Vriance = Between person variance + Within person variance = 7,64 x 10" + 2,06 x 10 = 22 x 10$ For the mean of N 5 observations from the same person, the Variance of Mean would then = Between person variance + Within person variance/N or 7,64 x 1 0 + 2 06 x 10 /N. Based on these calculations, one can then determine if the within person variance of the mean is less than the between person component. As shown in the table above N=3 will achieve this aim since 2,06 x 10-/ 3 = 6,87 x 10- which is less than the4 15 x 10' variance of mean value 10 (3' line in table For sample sizes more than four, little is gained in these studies by increasing the number of anayses since each additional observation gives < 5% total reduction from a sample size of 1 A database has been compiled of the levels of the various biomarkers that are indicative of the patients at risk of developing kidney disease or other complications. Non 15 diabetic levels are about 1/3 to 1/4 of those seen in diabetics, as shown in Table L, In one embodiment, levels of CEL <0.042 (0,020-0.042, MGHI <0.103 (0:030-0.103) and CML <0.062 (0.033-0.062) indicate a 94% chance that the individual is protected from DN (in the lower tertile of change). The levels of the three products were significantly higher in the fast progressors 20 (upper quartile of GBM change) relative to the non-progressors. This analysis was performed by a Wilcoxon method. TABLE L AGEs as Early Indicators of DN Biomarker FP SP P- Value (All nM.) Mean SD Mean SD (Wilcoxon) CML 0.088 0 175 0003 022h± 0 23 MCIII 0,200 0.165 0040 14 t0.099 ±0.127 CEL 0,058 0.049 0,026 ±0015 ±0,015 Linear regression analysis of any product or products versus progressive thickening of the GBM (DN progression) shows that the square of the R (correlation coefficient), which is a measure of the degree of prediction for each bionarker, was greater for the three 5 biomarkers with Hbg Al c relative to Hbg Ale alone. For examplein Table 2, bAlc accounts for 4.7% of predictive value (0.047), CML 0.026 %, etc. The sum of the ihree biomarkers plus Ale was 11.6 % The value of measuring a biomarker one time is additive to Alc.
    15 TABLE 2. Linear Regression of AGEs as Early Indicators of DN % of explained variation (r-squared) Variables in Regression Model GBM MES CEL 0,026 0.002 CML 0 26 0.01 MGHI ( 006 0.005 HbAlc 0047 0 Al c + CEL 0.073 0 G29 Alc + CML 0.065 0034 Ale + MGHJ 0.051 0.31 Ale + CEL. CML MGHI 0.116 0 0 52 The other type of analysis is the Logistical Regression analysis which. allows one to 5 calculate an odds ratio This allows one to predict the increase in risk of progression to dN in a linear fashion relative to the level of each product. This is probably the closest to quantitative prediction of risk and as described herein, predicts a 10 to 50 % risk. The lower tertile of values for the three biornarkers showed that for CEL <0.042 (0,020-0.042), MGHI <0.03 (0.030-0.103) and CML <0.062 (0.033-0,062% there was a 94% 10 chance that the individual is protected from DN (in the lower tertile of change). Each of the three biomarkers are individually predictive of progression to DN in combination with HbA ic. and the sum of the increased predictive power of kidney change (increased GMB wvidth) is increased from 4.7 % for one measurement of A c, to 1 1.6% for the three biomarkers. This represents an increase of 7.9 % which is 2.5 fold or 247% greater 15 than A e alone.
    16 Analyses performed by logistical regression show the odds of progression of DN (y axis) relative to the levels of the 3 biomarkers (X axis) (Figures 2B--D), relative HbAlc (Figure 2A). The Odds ratio for progression for CEL, MGH Il and CML biomarkers is 68 to 92% 5 for each one standard deviation of change is provided in Table 3, below. TABLE 3. Odds Ratios for Prediction of Rapid Progression of DN by A, or AGEs Bioniarker Odds Ratio 95%Confidence Value of I SD Interval Change HAIC I.29 (0.82. 2.02) 1.74 CML (log) 195 (1.14, 3.35) 0,0157 CEL 1,72 (,06, 2,7)3 MGHI (log) 1.68 (104, 2,81) 0561 Ten native and internal stable heavy isotope substituted AGE and OP standards were 10 procured from commercially available sources, or by custom synthesis, to create a database, Commercially available standards include Carboxymethyl Lysine (CML), Carboxyethyl Lysine (CELV Glyox al Mythroimidazione ( M Methylgiyoxal Hydroirnidazolone (MG-HI), and 3 Deoxyglucosone Hydmomidazolone (3DGH). Oxidative end products (OP) include Methionine Sulfoxide (MetSO), 3-Natotrosine(3-NT) Amnoadipic acid (AAA.) and Dityrosine. 15 These precision diagnostic tests assess an individual diabetic patient's risk of developing specific complications associated with their disease. These precision diagnosd tests are preferably performed on blood samples submitted to a central clinical laboratory facility In the preferred embodiment, the tests identify the presence and amount of 10 AGEs and OPs biomolecules (biomarkers). A risk profile for the development of diabetic complications, based 20 on a comparison with data obtained from individuals without disease, as well as at various confirmed stages of disease, is constructed. This information is sent to the ordering physician to deliver improved care by specifically adjusting treatments to an individual parent's profile. With increasing focus on idividualized, cost-effective treatment plans for patients, access to this information with become increasingly valuable and necessary in the marketplace.
    17 11L Method of Diabetic Care The methods described herein allow selection of risk and cost appropriate therapeutic regimens for diabetic individuals to achieve appropriate levels of glycemic control and delay or prevent associated DN complications. 5 These biomarkes have more than three times the predictive value for renal complications of the current "gold standard;" the Hemoglobin AlC . There is strong scientific evidence that these and other identified bionarkers could be early predictors of propensity to retinopathy, vascular disease, and risk of heart attack and stroke. Diagnostic tests that predict early progression of these complications before symptoms are visible allow doctors and patients to make individual 10 adjustments in treatments and behavior that could signiffeantly improve outcomes. Enhanced outcomes will improve lives, and could save billions of health care dollars annually, The current paradigm for diabetes care is one of "one size fits all." Parameters are set for all patients, and aggressive specialized treatments are offered either to those who can pay for them or for those who are already experiencing significant conp. ications. The traItment of 15 diabetes is often done with tests that provide retrospective information about what has already happened, followed by "catch up" treatments to deal with problems, rather than proactive individualized treatments to prevent those that are to come. Although aggressive diabetes treatments that are required for highly susceptible individuals can be cost effective in the long run, they are sometimes more expensive in the short run, and are likely to be accompanied by a 20 higher risk of hypoglycemia and drug side effects. Therefore early identification of high-risk individuals is necessary to balance the potential benefit against the increased risk and expense of new pharmacologi agents and newly evolving high-tech treatments The glycation/oxidation based diagnostic assays should significantly change preventative interventions by allowing the identification those at high or low risk of diabetic complications 25 during the earliest stages of diabetes Risk and cost appropriate therapeutic regimens can then be implemented to achieve appropriate levels of glyceric control For example, those identified as being most susceptible to complications could have more St ruaent goals for glycemic control than is generally achieved (Ale <6.0%), by initiating intensive insulin delivery and monitoring systems; pancreatic transplants or the artificial pancreas closer to diabetes onset T hese risky 18 goals could be justified by the observation that individuals with blood sugars this close to normal, do not develop diabetic complications irrespetive of genetic predisposition. Early more aggressive treatment of other vascular risk factors, or specialized diets with low levels of AGEs, could also be considered. Considering the high risk profiles of individuals 5 detected with the testing, different guidelines for risk of new therapeutic agents may also be justified. The development of therapeutic approaches that could block offending toxic chemical pathways to delay or arrest complications could also be stimulated by information provided by these studies on basic biochemical mechanisms and pathways responsible for diabetic comnplications 10 In other embodiments, these individuals could be administered medications that modify the renin-angiotensin systern (Mauer, et al, Journal ofthe ReninAngiorten Aldosterone Svste,, 3:262-69 (2002)), cholesterol and VLDL levels can be initiated (Almuti et al., Intenationral Joumrnal ofCardtiology, 109(1):7-15 (2006); Degenhardt, et al, Celldar & Mol ecular Biology, 447):1139-45 (1998); Rosario, et al Current Diabetes Reports, 15 6(6};455-62 (2006)). Alternatively, or in addition, speciahzed diets with low levels of AGEs or oxidative products (Koschinsky, Proc. Na. Acad. Sci. USA, 94(12):6474-6479 (1997)) can be used. Important information provided by these studio s on basic biochemical mechanisms and pathways responsible for diabetic complications could also stimulate development of therapeutic approaches that could modify offending toxic chemical pathways to delay or 20 arrest DN, DR, and CVD, Examples of drugs that can be used to modify diabetes management based on the sub jects risk of developing DN include, but are not limited to, Metformin (Beisswenge:r et al, Diab etes and Metabolisnt 29:6S95-6S103 (2003) Beisswenger, et lo Diabe t es, 48:198-202 (1999) Arinoguanidine (Lo, Anuno Acids, 5-72 (1993.); Hirsh, Jet al. CarbohYd. Res., 25 2 125-130 (1992.), Brownlee, et al., Diabees Care, 15(12)I 835-43 (1992); Hammes, et al., Proc, Nat, Acad Sc I8 155-11558 (1990; Thiamine and Benfotiamiine (Hammes, et al, Nature Med., 9(3):294-299 (2003) Levels of biornarkers of glycation and oxidaive stress are risk factors for the rate of development and progression to advanced diabetes retinopathy(DR) and nephropahy(DN) 30 over time. Levels of glycation and oxidative stress biomarkers are also risk factors for the 19 ultimate development of cardiovascular disease (CVD)in Type I diabetes, The effects of biochemical biomarkers of glycation and oxidation on defined outcomes can be assessed using a case-cohort design involving DR, DN and CVD cases and controls. For each selected subject, blood plasma and unne samples obtained at multiple specified times are eniployed, for 5 example, DCCT randomtzation; DCCT one year visit; DCCI' closeout (= EDIC baseline) and EDIC year one. This will provide adequate sample numbers and distribution to be representative of each biomarker. This can be used to assess the risk factors for the progression of microvascular and cardiovascular disease in type 1 diabetes. The three primary outcomes are the development of 10 advanced retinopathy; (proliferative diabetic retinopathy (PDR) detectable via funds photography or the requirement far pan-retinal photocoagulation (laser)), nephropathy; (the development (macro) albuminuria (>300 mg albumin/24 h) or endaage renal disea se (ESRD)); and the occurrence of a cardiovascular disease event. Albumin excretion rate is assessed from a four hour timed measurement of albumin excretion rate annually during DCCT and every other 15 year (half/year) during EDIC. The composite cardiovascular outcome includes fatal or non-fatal myocardial infarction or stroke, ischemic angina, revascularization, or silent MI detected on an annual ECG, A case-control design provides an efficient method to test the above aims for each of the three outcomes (case definitions> However, a simple random sample of controls from among 20 those event-free at the end of the trial would be biased owning to a longer average duration of exposure than the cases, A nested case-control study avoids this bias by randomly sampling controls from among those at risk at the time each selected case is observed. The data is then analyzed using a conditional regression model stratified y ase-control st or equivalently, a like-stratified Cox proportional hazards model [For each outcome, 12.5 cases with 250 controls 25 will provide 85% power to detect an odds ratio of ,39 per SD difference at the 0.05 level two sided. With three separate case definitions three separate nested case control sub-studies could require up to 3x375= 1125 subjets. Alternately a case-cohort aproah an be employed in which a single baseline randomly sampled "sub- ohoW' is selee d from the full cohort to provide a basis fox controls for each ease definition The efficienc (power) of this design for 30 given case-control sample sizes is equivalent to that of a nested. case-control study of the same 20 size. Thus, a case-cohort design that yields approximately 250 controls for each case definition will provide excellent power to detect meaningful associations of bionarkers with each of these outcomes. A case-cohort of 350 subjects with a 2:1 ratio of secondary to primary cohort subjects 5 was randomly selected since about twice as many cases of each type occurred in the secondary than primary cohorts. This provided some cases of DR, DN and CVD. Additional cases of each type were selected from the remaining 1091 subjects necessary to obtain It least 125 cases of each type. it was not possible to do so exactly because some subjects who were cases by one criterion were also cases from another. Then, for each case definition, 250 or so controls were 10 sampled The table provides the numbers of cases and controls within the primary and secondary cohorts, and total that were selected, 21 TABLE 4: Numbers of Cases and Controls with Primary and Secondary Cohorts. Total Primary Secondary CVD 381 159 222 5 Cases 127 53 74 Control 254 106 148 PDR 375 108 267 Cases 125 27 98 Control 250 81 169 10 Albuminiria 375 148 227 Cases 125 48 77 Control 250 100 150 Since many cases and controls for one outcome are also cases or controls for another 15 outcome, the total study with three sets of cases and controls comprises a total of only 546 subjects. 200 from the primary prevention cohort and 346 from the secondary intervention cohort it includes all cases of CVD observed at the time the sample was drawn and random samples of 125 of the DR and DN cases observed at that time. For each of the three case definitions, a modification of the Cox proportional hazards model for case-cohort sampling will 20 be used to assess the relative risk per SD of the biomarkers at each tie when added individually to models with and without corresponding longitudinal measures of HbAI Models will also adjusted for primary/secondary cohort, duration of diabetes on entry and the entry level of HbA Models will be fit using just the baseline levels of a biomarker and then also using the values of the biomarker at the three addiional tirne points as a time dependent. 25 covariate. The latter will be used to assess whether the baseline biomarker alone confers additional sk independently of the longitudinal NbA, Models can also be used to evaluate the effects of the set of biomarkers jointly, Before doing so, collineanity diagnostius vl be applied to ensure that there is not a degree of linear dependence (inter-correlation) that leads to variance inflation in the estimates. If so, within each 30 group of related markers the one with the strongest effects will be employed jointly with those 22 from other groups. A likelihood ratio test will then assess whether the final set of biomarkers contributes significantly to a model that also contains the longitudinal HbA, values, The present invention will be further understood by reference to the following non-limiting 5 examples. Example . The relationship between plasma levels of AGEs and OPs and nephropathy progression/non-progression Materials and Methods 10 Instrumentation: HPLC- Agilent 1200 Series Binary Pump., Autosampler, Degasser and Thermostatted Column Chamber; QQQ- Agilent 6410. Materials: MS grade water and methanol are from Honeywell. 15 Heptafluorobutyri acid (HFBA) LC grade, Fisher, (Pierce Chemical) # PI-53104 HPLC Colunn- Gemini-NX 3u C18, 4,6mm x 250 mm Phenomenex #000-4453-E. Since the analysis of 9 proposed bionarkers by LC-MS/MS is done on a single run, and requires the addition of heavy isotope internal standards for accurate quantification, the required light and heavy standards for nine biomarkers were obtained as follows Light and 20 heavy (N, 5) 3DG-I- was obtained from Organix in Essex UK. Heavy MG HI and G-H,L was produced by NeoMPS in France. Methionine sulfoxide and dityrosine were synthesized, and light and heavy standards of the remaining 5 biomarkers (Pentosidine. CEL CML. MI and GH) were purchased from available commercial sources. 2-namino adipic acid, a stable lysine derived end product which is an excellent indicator of oxidative stress was obtained from Sell 25 and Monnier at Case Western Reserve University in Clevelnd OH. (Beisswenager, et al, Diabetes, 54:3274-3281 (2005). The addition of heavy isotope internal standards for accurate quantification is required since the analysis of these 9 proposed bionarkers by LC-MSfMS is done on a single run, Methods: 30 Measurement of Bioniarkers: 23 The methods developed for the concurrent quantitative measurement of biomarkers indicative of protein glycation, oxidation, and nitrosative damage (Ahmed, et al., Biochemical Journl, 364(Pt 1): 1-14 (2002) were modified by employing a single 2 0 X 250 mm Synergy 4 micron 80A column (Phenorenex, USA) with a mobile phase of Methanol/H 2 0 gradient 5 with 0.29% heptafluorobutyric acid on the Agilent Model 1200 HPLC, with a total analysis time of 60 min on an Agilent Model 6410 triple quadrupole Mass Spectrometer (QQQ1, and approach not used in the art in measuring AGEs or oxidation products, An important change in this protocol relative to known methods for measuring AGEs is the completely different stationary phase used i.e., C8 (i.e., the Synergy 4 micron SA 10 column). This column chang resulted in use of mobile phase conditions which differ from prior art methods i.e, the use of heptafluorobutyric acid as an ion pairing agent. The combination of column type and mobile phase also allowed the sample analysis to be performed with a single column relative as opposed requiring 2 columns ie., samples could successfully be run using one column- two columns were not required, 15 The HPLC and QQQ conditions are shown below. HPLC Conditions: Mobile Phase: Solvent A= 95% 0.29% HFBA in water /5% 0.29% HFBA in methanol. Solvent B= 0.29% HFBA in methanol, Flow rate = 0.25ml/min Pump Time Table: 20 Time Solvent Ratio B 0- ............. 3 -......... . ...... 0 6.....................13 2 ...... ...........- 29 25 35 ......... ......... 100 45.......... . .00 50..... .,...... 0 70. ............ .... 0 Column Temperature: 28 C 30 24 QQQ Acquisition Parameters: The Agilent 6410 MS/MS, equipped with an ESI source was operating in the positive mode under the following conditons: Drying gas was at 350C with a flowrate of I0L /min, Nebulizer pressure was 40 psi and capilary voltage was set for 4000 for all compounds. 5 Detailed compound analytical parameters are shown in Table 4 Using isotopic dilution analysis quantitation of samples was achieved by reading from calibration curves derived from relative response Vs relative concentration to the heavy standard. Heavy standards were added to plasma or urine filtrates at final concentrations from I to 6 uM in concordance with the expected physiological concentrations and range of 10 the standard curves. The order of elution of the compounds is as shown in Table 5. This table also shows the coefficient of variation (COV) for repeated between day measurements of the analytes, as well as the lower linit of detection (LLOD) and lower limit of Quantitation (LLOQ) for each analvie. 1.5 Table 4; Compound Analytical Parameters for Mass Spectrometry Compound 1 Transition Fragmentor V Collision V CML (Quantifier) 2 205i841 100 22 CML (Qualifier)" 2051-13011 100 d4 CML (Isotope 2091-881 , 100 22 CEL 219.1-84.1 100 22 CEL 2191-130,1 100 8 CEL223.88 100 22 MetSO 166 1-74. 1 80 7 MetSO 166.1-1021 80 1 d3 MetSO 169.74I 80 7 3-NT 227-181 94 8 3-NT 27- 117 911 20 6V 3 3-NT 233-187 94 8 MG-Hi 229-66 .1 104 13 MG-Hi 229-114 87 12 25 d 3M G:H1j 232.1-169.2 104 13 MG4-H2 229-116 87 12 MG-H2 - 229-114 87 12 -- 232,11692 104 13 MG-H3 229-114.1 87 12 MG-H3 229-116 87 12 d3 MG-Hi 232,1469.2 104 3 DiTyr 361.1-315,1 100 10 DiTyr 361.1-254,1 100 8 2C 3 DiTyr 363.1-316.1 118 12 GH 1215-152 80 9 G-HI 215-116 80 5 2C' 3 G-HI 217.2-1541 104 12 G4H2 215.1-116A 80 5 G~H2 215.l100. 100 10 C G-H 217.2-1541 04 12 S-KS . 215.1-100.i 100 10 G-H3 215so162 805 2CJ GHI 217.2-154.1 204 12 3DG~H 319.1-204.2 120 14 3DG-H 319.1-116.1 120 22 6C4N 5 3DG41 329.1-208,1 120 14 AAA 1622-96.1 52 12 AAA 162.2-144,2 52 4 dS---A165.----1-2------ --------------- 12-- d3 AAA 165.2-101,2 55 2 1. Capillary voltage was set at 4000 for all transitions 2. Transitions remain in the same order throughout table Table 5: Validation of Analytical Method Compound Mean Between day LLOD 2 LLOQ2(n (nM) COVO/ 1 (nM) 26 MethSO 1610 11.2 64 210 AAA 1380 9.6 81 270 CIVL 110 10.1 10 34 CEL 62 10 8,6 28 3DG-H 450 10.9 40 130 G-H 1 22 11,6 3A 10 MG-H1 303 8.6 5.8 19 3NT LLOQ 22 71 DiTyr LLOQ 2.9 9.5 1. Calculated from replicate injections of a pooled plasma filtrate n=18 2. Calculated from the standard deviation of the response (SD) and the slope (S) of calibration curves. LLOD=33(SD/S) LLOQ=10(SD/S) Values represent the mean of five calibration curves
    R
    2 value exceeds 0.99 for all calibration curves Source Parameters: Mode: ESI Positive Gas Temp. (CMethods for sample preparation, 5 P'Ilasma sample preparatorn All of the samples used in this study were collected at the 5-year end of study NHS visit by a rigorous protocol, where blood was collected in EDTA containing tubes and immediately iced and centrifuged. Following centrifugation plasma was immediately separated from red blood cells (RJBCs) and snap-frozen on dry ice, and subsequently stored at -80'C until. these analyses were performed. 10 UMra/iuraes (free adducts): LC-MS/MS analyses were initially performed on the plasma "free fraction", prepared as the fi Irate following centrifugation through 10K cut-off Amicon filters. Adducresidues chemical y bound to plasina proteins: Since some of the products are acid labile, chermtally bound products are determined after exhaustive sequential enzymatic 15 digests with pepsin Pronase E, and Aminopeptidase/proldase (50 ptg protein equivalent) under nitrogen with controls for protease autolysis, as described by Ahmed, et al, iocheni 27 J 364(Pt 1)1i4(2002). AGE/OP analyses were performed following extensive sequental plasma digestion over 36 hours with 3 proteolytic enzymes under a nitrogen atmosphere (Ahned, et aL, Diabetologia, 48:15901603 (2005)) to investigate the protein "bound" fraction. 5 Urine sample Prepararion: For analysis of the biomarkers in urine, 4 samples per subject distributed over the 5 year NIS study, were tested. Preparation qf urine sanpies to measure excretin of adducts: For determination of the AGE and OP bionarker profile in urine, a filtrate prepared by centrifugation at 4"C through microspin filters (10,000 MW filter cut-off) as described by Ahmed, et al., Diabetologia, 10 48:1590-1603 (2005) were used. Urine creatinine levels can be determined to provide uniform expression of product/creatinine urine analyte content. LC-MS/MS was performed on the NtHS urine samples which provided an initial total population of 107 subjects consisting of nephropathy progressors (n=37), and non-progressors (n=70), The studies were completed (220 ( 5 5x4) additional analyses) over a 10-week period, 15 utilizing a throughput of 24 samples per week The statistical tests were done on the full sample (n=107 subjects) with a two-sided alpha =01I The assumption was that the SD in the whole study sample is the same as the sub-sanrple (N=52 The urinary and serum "free fraction" determinations also allow the calculation of renal clearance rates of each analyte. 20 Results A. Plasma Pdtrates (free Fraction): Since it is the best early structural predictor of DN clinical progression, change In glomerular basement membrane (GBM) width from baseline to 5 years in the MIS population measured in electron micrographs of renal biopsies, was the primary endpoint Mesangial 25 fractional volume was also measured. Fast progresors (FP) were defined as the upper quartile (n = 24) of GBM thickening and others as slow progressors (SP), AGEs [3-deoxyglucosone and methylglyoxal hydroinidazolones (D031H, MGH i)] and carboxymethyl and ethyl lysine (CML, CELl and oxidation products [methionine sulfoxide and 2 Aminoadipic Acidl were measured by liquid chromatography, triple quadruple mass spectroscopy on 10 K plasma 30 filtrates on 102 samples at year 5, It was found that MGHI, CEL and CML levels were 28 significantly higher in GBM-defired FP relative to SP, No AGE or OP predicted niesangial expansion in these studies. These results show three AGEs (Table 6; Figure 3) as early indicators of progression of important DN lesions. The other AGE and oxidative biomarkers measured in this study 5 did not correlate with DN progression (Table 5). TABLE 6. Plasina Biomarker Levels in Fast and Slow Nephropathy Progressors: Based on rates of GBMI thickening over 5 years Biomarker Fast Slow P- Value Number (All JM) Mean t SD Mean * SD Wilcoxon Progressors/Now ____ -_ -- _ -lP rogressors CML 0.088 0.075 0.003* 22/79 *0 022 *0.023 GHI 0.013 )0O 0, 16 -2/79 ±0-001 ±0.002 - - ----- --- -- ----------- _______________ -_____________________ MGHI 0.2005 160504* 22/79 ±0.099 ±0127 CEL 0.058 0.049 0.026* I 22/79 *0. 015 ±0.01.5 3DGHI 0382 0330 0- 229 ±0154 ±0. 156 MethSO 0.931 0-979 0,97 2279 0.304 +0348 10 Further analyses supporting the value of these three biomarkers in predicting early diabetic nephropathy are the observations that IbAlc at year 5 accounted for 4.7% of the variation in GBM width (R2, but the proportion of variation iin GBM width accounted for was increased to 11.6% when MGHI, CEL, and CML were added to the model (7.9% increase) Further, these analyses revealed MGHI as a significant independent predictor of 15 GBM increase. These findings indicate that MGHI, CEL. and CML are consistently low in those who are protected from progression; thus the bionarkers should identify those protected from DN, The ordered data for MGlH 1 shows that 2 of 31 (6% OF LOWER TERTILE) MGFW values of fast nephropathy progressors are in the bottom 30% (31 of 103) of the ordered values. but 20 MGHI values of 29 of 31 (94%) slow nephropathy progressors make up this bottom 30%.
    2 9 This was also true of the findings for CEL and CML, where 30 of 32 (94%) of the lowest values were seen in slow progressr for both biomarkers. The levels of these 3 hiomarkers were also compared to previously obtained levels for Non-progressors (Ahned, et a],, Diabaologia, 48:1590-1603 (2005); Ahmed, et al Scie 44(12):5287-92 (2003)), As 5 shown in Table 7 these studies show that the mean levels for the protected group (DN non progressors) are similar or slightly higher than levels seen in non-diabetic controls, TABLE 7- AGEs in Diabetics compared to Non Diabetic subjects CML CEL MG-Hi #Type I diabetes n= 106 78 51 172 Trype I diabetes n= 21 97 72 331 * Non-diabetic Subjects. N=6 23 35 110 4 Non diabetic Subjects 27 25 43 Lower Tertile of DN 52.4t7_5 34.5±61 7i.9±232 progression n=32 # Data from present study * Data from Ahmed Riochen Soc Trans, 31 (Pt 6):1417-22 (2003) 10 4 Data from Ahmed et al. Diabetologia, 48:1590-1603 (2005) The results show that the level of three AGEs in plasma filtrates (but no OPs) alone or in addition to HbA lc combination, that the three major AGEs, are important predictors (in addition to AHAlc) of progression to DN. B. Results on biomarkers in extensively digested plasma samples to 15 investigate their relationship with progression of DN. Similar statistical analyses were performed to determine if AGEs and OP (measured on extensive plasma digests of 102 samples, performed as described by Abmed, et al., Diabeoicgia, 48 i590-1603 (2005), show any correlaion with progression of DN defined by GBM thickening or mesangial expansion, Fast progressors (FP) were defined as the upper 20 quartile (n =24) of GBM thickeming and the remanider as slow progressors (SP). The same products were measured as performed on the "plasma filtrate" except for 2-amino adipic acid 30 (AAA) which was not measurable on digests, and (1E11 which was below the limits of detectability. See Table 5. As shown in Table 8 below, there was no correlation between levels of biomarkers in plasma digests and progression of DN. TABLE 8- Statistical Data for Levels of Bioinarkers in Plasma Digest t-test Wilcoxon CML 0,62 0.75 MethSO 0.28 0.36 3DG-H 0.61 0.50 CEL 0.70 0.61 MG-F 12 022 030 The degree of elevation in protein bound AGFs and OPs were modest, relative to that seen in either plasma of urinary "free" fractions suggsting less modification of relatively short half-life plasma proteins in diabetes. See Table 9. TABLE 9. Plasma Hydrolysates Biomarker levels in Plasma in type I diabetes and 10 controls CML MetSO 3DG-t C EL G-l MG-1 DMS NIS samples (Tve I Diabetes) 0.031 12.8 1.03 0.0019 0,024 DMS normal controls n=9 0.024 994 0.93 0.0011 0.022 Values expressed as mean nmoles/mole AA 31 The data in Table 8 shows that none of these protein-bound AGE or OP bionarkers in plasma were related to nephropathy progression or non-progression, based on the degree of GBM change or MES change, C. Results from completion of analysis of urinary AGEs and oxidation 5 products For these studies, fas p e were define as the upper 37 subjects with the gpretest egree of G3M Ickeing and ressrs P) as t e re i 4nr. Tbe analyses were done on the dataset that was corrected for a few outliers where a value was excluded if it was> 2 SD beyond that person s own individual mean. 10 The mean values for the 8 measured biomarkers by Progressor (fast)/Non-Progressor (slow) are shown in Table 10. Of those 8, only urinary levels of CEL (p=0.04) showed a significant difference between groups. CML is next closest (p=0. 10), then DiTyr (p=0,16), with all other p-values > 0.25. To further confirm these results, the analyses were repeated using non -parametric methods (Wilcoxon Rank-Sum. Test), and only CEL (p=0.02) was 15 different between groups. No biornarker correlated with the degree of mesangial expansion over 5 years.
    32 TABLE10. Urinary Biomarker Levels in Nephropathy Progressors (FaIst) and Non P1rogressors ( Sl ow) . . ...... ...... . ....5. ..... A. . .. . .. . .. . .. . .. . .. . . N.. .... ....... .. ... .............. ........ g ...... ...... ...... .e d D % m m m M ............. 3 5. 3.............4.2 s......................3 ... y..s.V........di..r Nm I" "..................... .. ... .. .... :b . M e . .. d ... ... a .. ... ... ... ... ... ... ... .. fast.......5.....2........9 ........ I 9 ........ 3 2 6 A.. d ............ :........... . g. N ....... ..... ...... ...... pp. ............. e..........a.i.. .a. .......... ............. 4..A..7..... 2...4 e....... ... 9. ...... 9.... ................ 9........, ... .. .... . ... ... A n .. ... .a n .. .... .. . .. .. dqow am gp9 3-x 535 7A 00 25
    -------
    33 TABLE 10 cont. Urinary Biomarker Levels in Nephropathy Progressors (Fast) anad Non-Progressors (Slow) A.. a.y.... .. a............. ... N................ ....................... ................ S............M...i.. .... s.. ..... 3..... 32 3 ....... .. ... .... 9............. A na.. . .. . . .. . .. . . .. . .. . . .s. . .. .. ..... .. .. ... ..... .............. ........ ....... D.Na m M a i fas... .......... 3............. ...... 4........ 4. ...... w. a.......d ad e ..... A..... ....... ..... N.. ... ..... . . ............... ....... ...... ...... ........... .............. ........... gX-p .......... N.e..i~ W i m a fm 35 M 729 L&: 39 16 ............. l...............o w................... .... 6 6 A S2 3 5 Althogh stasticalsignifiance ws.obtaned.w..CEL,.wen.thee.resuts.wer adjused.fr.. ter.vaiabl..k..w..oeffet.DNprogessio(gene..dabe.e.durtion.age and ............... Hbl teecreAosbtenCLadD PrgesoNn-orssnwren .... nger.. s.titca. s.gnificain (Table 11............
    34 TABLE i Univariate and Multivariate analysis of CEL in Urine and GBM thickening CEL single var (GBM, age adD Beta p-values Biomarker (CEL) L17 0,039 multivariate (GBM, age adD Riomarker (CEL) 0 0.82 HbAle 4.43 <0.0001 Duration .15 <0.0001 Gender (if F) -6.45 0.0033 These outcomes indicate that carefully quantified, specific urinary AGEs and 5 Oxidative biomarkers do not show a statistically significant independent relationship with progression of biopsy proven diabetic nephropathy. Unless defined otherwise, all technical and scientific terms used herein have the same meanings as commonly understood by one of skill In the art to which the disclosed invention belongs. Publications cited herein and the materials for which they are cited are specifically 1.0 incorporated by reference. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific em bodiments of the invention described herein. Such equivalents are intended to be encompassed by the following claims.
    权利要求:
    Claims (15)
    [1] 1. A method for determining the risk or rate of an individual of developing diabetic nephropathy, eye disease or cardiovascular complications comprising: 5 determining the levels of two or more biomarkers purified from a biological sample, wherein the biomarkers are selected from the group consisting of lysine advanced glycation end products, arginine advanced glycation end products, and oxidation products, and comparing the metabolite levels to standard values, wherein the level of the biomarkers indicates the risk of developing diabetic complications or the rate of developing 10 diabetic complications.
    [2] 2. The method of claim 1, wherein the biomarker is selected from the group consisting of Ne-carboxy methyl-lysine (CML), NE-carboxy ethyl-lysine (CEL), Glyoxal hydroimidazolone (GHl), Methylglyoxal hydroimidazolone (MGHI); 3-Deoxyglucosone Hydroimidazolone (3DGH), methionine sulfoxide (MetSO), and 3-nitrotyrosine (3-NT). 15 3. The method of claim I wherein the advanced glycation end products are selected from the group consisting of N,-(1-carboxyethyl-lysine (CEL), methylglyoxyl-derived hydroimidazolone (MGHI) and Ncarboxymethyllysine (CML).
    [3] 4. The method of claim 3 wherein the plasma levels of CML, CEL, and MG-H1, alone or in combination with the level of HbA I c, are measured as indicators of early progression of 20 diabetic nephropathy.
    [4] 5. The method of claim 4 wherein values of CEL of less than 0.042, MGHI less than 0.103 and CML less than 0.062), indicate that the individual has a low risk or slow rate of development of diabetic nephropathy.
    [5] 6. The method of claim 5 wherein values of CEL between 0.020-0.042, MGHI between 25 0.030-0.103 and CML between 0.033-0.062, indicate that the individual has a low risk or slow rate of development of diabetic nephropathy.
    [6] 7. The method of claim 1 comprising obtaining the biological sample from an individual and determining the level of the biomarkers using Liquid Chromatography/Triple Quadrupole Mass Spectroscopy (LC-MS/MS) to purify and quantify the biomarkers. 36
    [7] 8. The method of claim 1 wherein the sample is a urine sample or a plasma sample9. The method of claim 1 wherein the sample is a plasma ultrafiltrate.
    [8] 10. The method of claim 7 wherein the LC-MS/MS stationary phase is C18 with heptafluorobutyric acid being the ion pairing agent. 5 11. The method of claim 1 wherein the individual is determined to be at risk of developing diabetic nephropathy
    [9] 12. The method of claim 1 wherein the individual is determined to be at risk of developing diabetic retinopathy
    [10] 13. The method of claim 1 wherein the individual is determined to be at risk of developing 10 diabetic cardiovascular complications.
    [11] 14. The method of claim 1 further comprising providing a report with the risk or rate of development of diabetic complications.
    [12] 15. The method of claim 14 further comprising providing recommended treatment options for the individual at risk or having an elevated rate of development of diabetic complications. 15 16. The method of claim 15 wherein the treatment options are selected from the group consisting of glucose lowering agents, medications that modify the renin-angiotensin system, and specialized diets with low levels of AGEs or oxidative products.
    [13] 17. The method of claim 1 wherein the individual has not been diagnosed with diabetic nephropathy, eye disease or cardiovascular complications. 20 18. A kit comprising reagents for use in testing a sample using the method of any of claims 1-10.
    [14] 19. The kit of claim 18 comprising reagents for testing for levels of two or more biomarkers selected from the group consisting of NE-carboxy methyl-lysine (CML), NE carboxy ethyl-lysine (CEL), Glyoxal hydroimidazolone (GH1), Methylglyoxal 25 hydroimidazolone (MGH1); 3-Deoxyglucosone Hydroimidazolone (3DGH), methionine sulfoxide (MetSO), and 3-nitrotyrosine (3-NT).
    [15] 20. The kit of claim 18 comprising reagents for determining the level of the biomarkers using Liquid Chromatography/Triple Quadrupole Mass Spectroscopy (LC-MS/MS).
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    同族专利:
    公开号 | 公开日
    AU2013206252B2|2016-01-28|
    ES2703145T3|2019-03-07|
    US20130345175A1|2013-12-26|
    CA2818328C|2017-09-05|
    EP2706360A1|2014-03-12|
    TR201820019T4|2019-01-21|
    JP5952223B2|2016-07-13|
    JP2013257328A|2013-12-26|
    EP2706360B1|2018-10-03|
    US10018591B2|2018-07-10|
    DK2706360T3|2019-01-21|
    CA2818328A1|2013-12-11|
    US20180306747A1|2018-10-25|
    JP2015172603A|2015-10-01|
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    法律状态:
    2016-05-26| FGA| Letters patent sealed or granted (standard patent)|
    2018-01-18| MK14| Patent ceased section 143(a) (annual fees not paid) or expired|
    2018-04-05| NA| Applications received for extensions of time, section 223|Free format text: AN APPLICATION TO EXTEND THE TIME FROM 11 JUN 2017 TO 11 FEB 2018 IN WHICH TO PAY A RENEWAL FEE HAS BEEN FILED |
    2018-06-28| NB| Applications allowed - extensions of time section 223(2)|Free format text: THE TIME IN WHICH TO PAY A RENEWAL FEE HAS BEEN EXTENDED TO 11 FEB 2018 |
    优先权:
    申请号 | 申请日 | 专利标题
    US201261658218P| true| 2012-06-11|2012-06-11||
    US61/658,218||2012-06-11||
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