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. Author manuscript; available in PMC: 2018 Jan 16.
Published in final edited form as: Crit Rev Clin Lab Sci. 2009;46(3):129–152. doi: 10.1080/10408360902805261

Table 1.

Overview of clinical proteomic studies using CE-MS

Study groups
Origin of sequenced biomarkers**
Disease Aim Discovery phase Validation phase Performance***: Reference
Diabetes type 2, Diabetic nephropathy (DN*) Renal damage evaluation 66 diabetics type 2 w/albuminuria 46 diabetics type 2 w/o albuminuria 39 HC – DN makers:

  • serum albumin ↑

  • uromodulin ↓

  • insulin like peptide 3 ↓

 Renal damage pattern conspicuity 35% diabetics w/albuminuria 4% diabetics w/o albuminuria 95
Diabetes type 1, DN Diabetes staging and early diagnosis of DN 44 diabetics type 1 (>5 yrs diabetes duration) 9HC – – Early detection of diabetic renal alterations 96
Membranous glomerulonephritis (MGN) SELDI and CE-MS comparison for MGN-specific urinary peptide biomarker search 8 MGN, 8 HC – – Numbers of potential MGN-specific urinary peptide biomarkers identified by SELDI: 3 and by CE-MS: 200 35
Focal segmental glomerulosclerosis (FSGS)
Minimal change disease (MCD) MGN		 Establisment of normal and disease-specific urinary peptide profiles 16 MCD, 18 MGN, 10 FSGS, 57 HC – – HC vs. MCD/FSGS vs. MGN 84% true classification after cross-validation: 94% HC, 71% MCD/FSGS, 93% MGN 41,62
IgA nephropathy (IgA-N) IgA-N diagnosis 45 IgA-N, 13 MGN, 57 HC – IgA-N markers:

  • serum albumin ↑

 HC vs. IgA-N 100% sensitivity, 90% specificity MGN vs. IgA-N 77% sensitivity, 100% specificity FSGS, MCD, DN vs. IgA-N 100% sensitivity/specificity 77, 80
Chronic renal diseases IgA-N differentiation 45 IgA-N, 25 vasculitis, 30 FSGS, 106 DN, 24 lupus nephritis, 7 hypertension, 4 nephrosclerosis, 3 amyloidosis 49 blinded samples including IgA-N, HSP w/ and w/o nephritis, HCV-induced GN, non-IgA-N GN and HC Renal damage markers:

  • serum albumin ↑

  • alpha-1-antitrypsin ↑

  • collagens ↓

  • uromodulin ↓

 IgA-N vs. others 90% sensitivity, 82% specificity 79
Diabetes type 2, DN DN diagnosis and candesartan therapy monitoring 20 normo −DR, 20 normo +DR, 20 micro +DR, 18 macro +DR – Candesartan influenced markers:

  • serum albumin ↓

  • collagen 1 (II) alpha ↑

  • uromodulin ↓

 Profit of candesartan treatment 86% to 54% decrease of diabetic proteome pattern recognition 81
Diabetes, DN, nondiabetic proteinuric renal diseases Differentiation of proteinuric renal diseases 30 normo, 29 micro, 30 macro/DN, 30 HC 211 blinded samples including normo/micro/macro diabetics (Diabetes), other macro diabetics (Macro), IgA-N/FSGS/MGN/MCD (Nondiabetic renal diseases), HC Diabetes and DN markers:

  • collagens ↓

  • uromodulin ↓

 HC vs. Diabetes 89% sensitivity, 91% specificity HC vs. Macro 97% sensitivity/specificity Diabetic vs. nondiabetic renal diseases 81% sensitivity, 91% specificity 82
Coronary artery disease (CAD) CAD diagnosis 30 patients w/angiographically confirmed CAD, 20 controls w/o CAD history, 233 healthy university recruits (prevention of center specific bias), 17 paired samples of hypertension/type II diabetes patients before and after 12 wkrampiril treatment (exclusion of medication effects) 47 blinded samples of patients w/angiographically confirmed CAD and of age-matched controls w/o CAD history CAD markers:

  • collagen 1 (I) alpha ↑

  • collagen 1 (III) alpha ↑

  • membrane-associated progesterone receptor component 1 ↑

Reduction by physical activity programs		 CAD vs. no CAD 98% sensitivity, 83% specificity 116,119
CAD and DN CAD risk prediction for diabetes type 1 15 diabetes type 1 w/CAD, 4 nondiabetes w/CAD, 19 type 1 diabetes w/o CAD, all from the CACTI study group [97] Angiographic follow-up of patients CAD risk markers:

  • collagen 1 (I) alpha ↑

  • collagen 1 (III) alpha ↑

 Prevalence of proteomic CAD score and CAD events: 2.2 (1.3-5.2) odds ratio (95% CI) in Prediction of CAD events 1.4 ± 1.3 yrs in advance 98
Acute tubulointerstitial rejection (AIR) Detection of AIR 29 w/o AIR/UTI, 19 w/subclinical or clinical AIR, 10 w/UTI 26 blinded samples w/o AIR/UTI, w/subclinical or clinical AIR and w/UTI Renal transplant markers:

  • collagen 5 (IV)

  • alpha ↑

 Correct classification: 8/10 w/o AIR/UTI, 6/9 w/subclinical or clinical AIR, 3/7 w/UTI 99
ANCA associated vasculitis (AAV) AAV diagnosis and therapy response AAV diagnosis: 18 active AAV, 200 HC, 225 other glomerular diseases AAV therapy response: 18 active AAV, 19 AAV in stable remission (>18mo) 40 blinded samples including active AAV, MGN, IgA-N, FSGS, MCD, lupus nephritis and HC AAV markers:

  • serum albumin ↑

  • alpha1-antitrypsin ↑

  • collagens ↓

  • haemoglobin (C- and N-term.) ↑

 sensitivity: 94%, specificity: 93% for AAV positive classification of only 3 IgA-N (n=18) Haubitz et al., submitted
Ureteropelvic junction obstruction (UPJ) UPJ prognosis 19 severe UPJ obstruction, 19 low-grade UPJ, 13 HC Prospective blinded study on 36 newborns UPJ markers:

  • proSAAS ↓

 Prediction of clinical outcome 94% precision (9 mo in advance, n=36) 101,102
Urothelial carcinoma Urothelial carcinoma prediction 46 urothelial carcinoma, 11 benign prostate hyperplasia, 22 HC Masked specificity assessment 366 including non-malignant genitourinary disease, renal cancer, prostate cancer and HC Prospective masked assessment nephrolithiasis and HC Urothelial carcinoma markers:

  • fibrinopeptide A ↑

 HC vs. urothelial carcinoma: 100% sensitivity/specificity non-malignant genitourinary disease vs. Urothelial Carcinoma: specificity range: 86–100% 46
Prostate cancer (PCa) PCa diagnosis 51 biopsy-proven PCa, 35 NDE 264 blinded samples of biopsy-proven PCa and NDE PCa markers:

  • collagen 1 (I) alpha ↓

  • collagen 1 (III) alpha ↓

  • •SPR1↓

  • Na+, K+-ATPase gamma chain ↓

 PCa vs. NDE89% sensitivity, 51% specificity combined with age score and free PSA91% sensitivity, 69% specificity 16
Urothelial bladder cancer (BCa) BCa staging 127 BCa (71 Tis-T1, 56 T2-T4), 11 genitourinary disease, 34 nephrolithiasis, 81 smokers, 171 HC 130 blinded samples of BCa-stages Tis-T1 and T2–T4 BCa stage markers:

  • membrane associated progesterone receptor component ↓

  • collagen 1 (I) alpha ↓

  • collagen 1 (III) alpha ↓

  • uromodulin ↓

 Muscle invasive (Tis-T1) vs. muscle non-invasive (T2-T4) tumors 81% sensitivity, 57% specificity combined with cytology 92% sensitivity, 68% specificity Schiffer et al., submitted
Acute graft-versus-host disease (aGvHD) aGvHD grade>I diagnosis after allogeneic HSCT 13 HSCTw/aGvHD grade > I, 50 HSCT w/o aGvHD, for nonspecific marker exclusion: 69 renal disease controls including IgA-N, DN, FSGS, MCD, MGN, vasculitis, SLE and 20 HC 599 blinded HSCT samples w/ and w/o aGvHD grade I–IV aGvHD grade>I markers:

  • collagen 1 (I) alpha ↓

  • collagen 1 (III) alpha ↑

 HSCT w/aGvHD grade > I vs. HSCT w/o aGvHD or w/aGvHD grade I 83% sensitivity, 76% specificity Correct classification of aGvH D>grade I even before clinical diagnosis 115
*

abbreviations used in the table: DR, diabetic retinopathy; GN, glomerulonephritis; HSP, Henoch-Schoenlein purpura; HSCT, hematopoietic stem cell transplantation; macro, macroalbuminuria; micro, microalbuminuria; NDE, no disease evidence; normo, normoalbuminuria; RTR, renal trasplant recipients; UTI, urinary tract infection.

**

peptides from the specified protiens,

***

if not otherwise indicated in blinded fashion.