Abstract:
BACKGROUND:During the proteomic era, one of the most rapidly growing areas in biomedical research is biomarker discovery, particularly using proteomic technologies. The urinary proteome is known to be a valuable field of study and has become one of the most attractive subdisciplines in clinical proteomics for human diseases. We have described the levels of protein biomarkers specific to diabetes mellitus type 2 in the Pakistani population using proteomic technology. METHODS:One hundred type 2 diabetes patients with 50 age- and sex-matched normal healthy controls were recruited from Sheikh Zayed Hospital, Lahore, Pakistan. Urinary proteins were analyzed by two-dimensional liquid chromatography, using chromatofocusing in the first dimension and reverse-phase chromatography in the second, followed by mass spectrometric analysis. Levels of the proteins, which were found to vary in the diabetes type 2 patients compared to the controls, were then determined by enzyme-linked immunosorbent assay in all the samples. RESULTS:Levels of transthyretin, α-1-microglobulin/bikunin precursor, and haptoglobin precursor decreased by 30.8%, 55.2%, and 81.45%, whereas levels of albumin, zinc α2 glycoprotein, retinol binding protein 4, and E-cadherin increased by 486.5%, 29.23%, 100%, and 693%, respectively, in the diabetes patients compared to the controls. CONCLUSIONS:Variation in the levels of these identified protein biomarkers have been reported in other pathological states. Assessment of the levels of these biomarkers will be helpful not only in early diagnosis but also in prognosis of diabetes mellitus type 2.
journal_name
Diabetes Technol Therjournal_title
Diabetes technology & therapeuticsauthors
Riaz S,Alam SS,Srai SK,Skinner V,Riaz A,Akhtar MWdoi
10.1089/dia.2010.0078subject
Has Abstractpub_date
2010-12-01 00:00:00pages
979-88issue
12eissn
1520-9156issn
1557-8593journal_volume
12pub_type
杂志文章,随机对照试验abstract:BACKGROUND:Prediction of glycemic profile is an important task for both early recognition of hypoglycemia and enhancement of the control algorithms for optimization of insulin infusion rate. Adaptive models for glucose prediction and recognition of hypoglycemia based on statistical and artificial intelligence technique...
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journal_title:Diabetes technology & therapeutics
pub_type: 杂志文章,多中心研究,随机对照试验
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journal_title:Diabetes technology & therapeutics
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pub_type: 杂志文章,随机对照试验
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journal_title:Diabetes technology & therapeutics
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journal_title:Diabetes technology & therapeutics
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journal_title:Diabetes technology & therapeutics
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更新日期:2006-02-01 00:00:00
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journal_title:Diabetes technology & therapeutics
pub_type: 杂志文章,随机对照试验
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journal_title:Diabetes technology & therapeutics
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journal_title:Diabetes technology & therapeutics
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journal_title:Diabetes technology & therapeutics
pub_type: 杂志文章,随机对照试验
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更新日期:2010-08-01 00:00:00
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pub_type: 杂志文章
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pub_type: 杂志文章
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pub_type: 临床试验,杂志文章,多中心研究
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abstract:BACKGROUND:Real-time continuous glucose monitoring (CGM) was studied in 140 adults with diabetes over a 12-week period of home use. Hemoglobin A(1c)(HbA1c) was measured on day 1 (baseline) and at weeks 6 and 12. METHODS:On day 1, participants received the CGM device (STS(R) System, DexCom, Inc., San Diego, CA) and und...
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pub_type: 临床试验,杂志文章,多中心研究
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更新日期:2007-06-01 00:00:00
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pub_type: 杂志文章,评审
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更新日期:2008-04-01 00:00:00
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更新日期:2011-06-01 00:00:00
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