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1.
Ann Clin Biochem ; 56(2): 204-209, 2019 03.
Article in English | MEDLINE | ID: mdl-30282464

ABSTRACT

BACKGROUND: To promote glycated albumin standardization, the Committee on Diabetes Mellitus Indices, Japan Society of Clinical Chemistry published the recommended reference method and is supplying the reference standard material (JCCRM 611). In this study, we evaluated the basic performance of 'standardized enzymatic method' that is traceable to JCCRM 611 and established a unit conversion formula from standardized glycated albumin value (mmol/mol) to the routinely used glycated albumin value (%). METHODS: To evaluate the accuracy and within-run reproducibility of the 'standardized enzymatic method', JCCRM 611 was measured 20 times. To establish the unit conversion formula, serum specimens with known HbA1c concentrations were collected, and their glycated albumin (%) and glycated albumin (mmol/mol) were measured. RESULTS: The accuracy of glycated albumin value of the 'standardized enzymatic method' was 100.8-103.0%. The within-run reproducibility CV (coefficient of variation, %) of glycated albumin value was 0.6-0.9%. In total, 240 serum specimens were collected, and the distribution of their HbA1c values was HbA1c ≤ 4.9% ( n = 23), 5.0-5.9% ( n = 50), 6.0-6.9%, ( n = 44), 7.0-7.9% ( n = 40), 8.0-8.9% ( n = 37) and ≥9.0% ( n = 46). The unit conversion equation was defined as: GA (%) = 0.05652 × GA (mmol/mol)-0.4217 ( r = 0.999). Glycated albumin (%) calculated by the equation was in close agreement (96.6-104.2%) with the actual measured glycated albumin (%). CONCLUSIONS: The glycated albumin unit conversion formula was established from mmol/mol to %. This formula provides an easy method to convert the unit and is expected to be useful for the standardization of glycated albumin measurement.


Subject(s)
Blood Chemical Analysis/standards , Serum Albumin/analysis , Glycation End Products, Advanced , Humans , Reference Standards , Reproducibility of Results , Glycated Serum Albumin
2.
Clin Exp Nephrol ; 20(2): 258-64, 2016 Apr.
Article in English | MEDLINE | ID: mdl-26349434

ABSTRACT

BACKGROUND: Determination of daily protein intake in the management of chronic kidney disease (CKD) requires precision. Inaccuracies in recording dietary intake occur, and estimation from total urea excretion presents hurdles owing to the difficulty of collecting whole urine for 24 h. Spot urine has been used for measuring daily sodium intake and urinary protein excretion. METHODS: In this cross-sectional study, we investigated whether urea nitrogen (UN) concentration in spot urine can be used to predict daily protein intake instead of the 24-h urine collection in 193 Japanese CKD patients (Stages G1-G5). After patient randomization into 2 datasets for the development and validation of models, bootstrapping was used to develop protein intake estimation models. RESULTS: The parameters for the candidate multivariate regression models were male gender, age, body mass index (BMI), diabetes mellitus, dyslipidemia, proteinuria, estimated glomerular filtration rate, serum albumin level, spot urinary UN and creatinine level, and spot urinary UN/creatinine levels. The final model contained BMI and spot urinary UN level. The final model was selected because of the higher correlation between the predicted and measured protein intakes r = 0.558 (95 % confidence interval 0.400, 0.683), and the smaller distribution of the difference between the measured and predicted protein intakes than those of the other models. CONCLUSION: The results suggest that UN concentration in spot urine may be used to estimate daily protein intake and that a prediction formula would be useful for nutritional control in CKD patients.


Subject(s)
Dietary Proteins/urine , Renal Insufficiency, Chronic/urine , Urea/urine , Adult , Aged , Cross-Sectional Studies , Female , Humans , Male , Middle Aged
3.
Metabolism ; 58(6): 843-7, 2009 Jun.
Article in English | MEDLINE | ID: mdl-19446113

ABSTRACT

In this study, we investigated the factors predicting poor glycemic control after an inpatient program. Using the hospital database from April 1999 to May 2003, we retrospectively identified patients with type 2 diabetes mellitus and hemoglobin A(1C) (HbA(1C)) of at least 8.0% at the time of admission for an inpatient program. In the primary analysis, factors potentially related to poor glycemic control (HbA(1C) > or =7.0%) at 6 months after admission were investigated. Stepwise multivariate regression analysis identified the duration of diabetes (odds ratio, 2.43; 95% confidence interval [CI], 1.54-3.82; P < .001), period from the first attendance at our hospital until admission (odds ratio, 1.60; 95% CI, 1.01-2.54; P = .047), and number of admissions (odds ratio, 2.28; 95% CI, 1.36-3.82; P = .002) as predictors of poor glycemic control. In the secondary analysis, factors related to poor glycemic response (an absolute decrease of HbA(1C) by <1.5% from the baseline) at 6 months after admission were investigated. Stepwise multivariate regression analysis identified the duration of diabetes (odds ratio, 2.17; 95% CI, 1.19-3.93; P = .011), period from the first attendance at our hospital until admission (odds ratio, 2.17; 95% CI, 1.43-3.29; P < .001), treatment of diabetes at discharge (oral hypoglycemic agents: odds ratio, 2.52; 95% CI, 1.15-5.51; P = .021; insulin: odds ratio, 4.44; 95% CI, 1.96-10.07; P < .001), baseline HbA(1C) (odds ratio, 0.44; 95% CI, 0.37-0.53; P < .001), and addition of new medications (odds ratio, 0.41; 95% CI, 0.27-0.62; P < .001) as predictors of poor glycemic control.


Subject(s)
Diabetes Mellitus, Type 2/diagnosis , Glycated Hemoglobin/analysis , Hyperglycemia/diagnosis , Aged , Diabetes Mellitus, Type 2/drug therapy , Female , Hospitalization , Humans , Hyperglycemia/drug therapy , Hypoglycemic Agents/therapeutic use , Inpatients , Male , Middle Aged , Multivariate Analysis , Odds Ratio , Prognosis , Retrospective Studies , Risk Factors
4.
J Clin Invest ; 115(12): 3494-505, 2005 Dec.
Article in English | MEDLINE | ID: mdl-16294222

ABSTRACT

Activation of inflammatory pathways may contribute to the beginning and the progression of both atherosclerosis and type 2 diabetes. Here we report a novel interaction between insulin action and control of inflammation, resulting in glucose intolerance and vascular inflammation and amenable to therapeutic modulation. In insulin receptor heterozygous (Insr+/-) mice, we identified the deficiency of tissue inhibitor of metalloproteinase 3 (Timp3, an inhibitor of both TNF-alpha-converting enzyme [TACE] and MMPs) as a common bond between glucose intolerance and vascular inflammation. Among Insr+/- mice, those that develop diabetes have reduced Timp3 and increased TACE activity. Unchecked TACE activity causes an increase in levels of soluble TNF-alpha, which subsequently promotes diabetes and vascular inflammation. Double heterozygous Insr+/-Timp3+/- mice develop mild hyperglycemia and hyperinsulinemia at 3 months and overt glucose intolerance and hyperinsulinemia at 6 months. A therapeutic role for Timp3/TACE modulation is supported by the observation that pharmacological inhibition of TACE led to marked reduction of hyperglycemia and vascular inflammation in Insr+/- diabetic mice, as well as by the observation of increased insulin sensitivity in Tace+/- mice compared with WT mice. Our results suggest that an interplay between reduced insulin action and unchecked TACE activity promotes diabetes and vascular inflammation.


Subject(s)
Inflammation/genetics , Receptor, Insulin/metabolism , Tissue Inhibitor of Metalloproteinase-3/genetics , Tissue Inhibitor of Metalloproteinase-3/physiology , Tumor Necrosis Factor-alpha/genetics , Analysis of Variance , Animals , Deoxyglucose/metabolism , Diabetes Mellitus/pathology , Electrophoresis, Polyacrylamide Gel , Gene Expression Profiling , Genetic Vectors , Glucose/metabolism , Glucose Tolerance Test , Glycogen/chemistry , Heterozygote , Homeostasis , Hyperglycemia/pathology , Hyperinsulinism/pathology , Insulin/metabolism , Liver/metabolism , Mice , Mice, Inbred C57BL , Mice, Transgenic , Muscle, Skeletal/pathology , Muscles/pathology , Phosphorylation , Promoter Regions, Genetic , Protein Binding , RNA/chemistry , RNA/metabolism , RNA, Messenger/metabolism , RNA, Small Interfering/metabolism , Reverse Transcriptase Polymerase Chain Reaction , Signal Transduction , Time Factors , Tissue Inhibitor of Metalloproteinase-3/metabolism , Tumor Necrosis Factor-alpha/metabolism
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