Association between the extent of urinary albumin excretion and glycaemic variability indices measured by continuous glucose monitoring

AIMS

The contribution of glycaemic variability to the microvascular complication of diabetes has not been established. We examined whether there is an independent association between indices of glycaemic variability in continuous glucose monitoring and extent of albuminuria.

METHODS

A total of 173 patients with Type 2 diabetes (without insulin therapy, n = 96; with insulin therapy, n = 77) who had unexplained large fluctuations in blood glucose values underwent three-day continuous glucose monitoring. We used a multinomial logistic regression model to determine whether the indices of glycaemic variability independently affected the odds of having a spot urine albumin/creatinine ratio of 30-299 mg/g and ≥ 300 mg/g.

RESULTS

Higher standard deviation (P = 0.002), mean of daily differences (P = 0.023) and mean amplitude of glycaemic excursion (P = 0.043) significantly increased the odds of having a urine albumin/creatinine ratio of ≥ 300 mg/g. In multivariable analysis, only higher standard deviation, but not mean amplitude of glycaemic excursion and mean of daily differences, independently increased the odds of having a urine albumin/creatinine ratio of ≥ 300 mg/g (P = 0.025). Coefficient of variation (sd/mean) was not associated with the odds of having a urine albumin/creatinine ratio of 30-299 or ≥ 300 mg/g.

CONCLUSIONS

The independent association between standard deviation and the extent of albuminuria was lost when the measures were normalized by mean glucose level. At least in terms of relative measures of glycaemic variability, we failed to demonstrate an independent association between glycaemic variability and albuminuria extent in patients with inadequately controlled Type 2 diabetes.

Structural equation model for the evaluation of national funding on R&D project of SMEs in consideration with MBNQA criteria

Financial support on the R&D in Science & Technology for SMEs at the governmental level plays a crucial role on the improvement of the national competitiveness. Korea Science & Engineering Foundation (KOSEF) has supported the R&D projects of SMEs with the competitive technology ability by way of the Science and Technology Promotion Fund. In this paper, we propose a structural equation model (SEM) to evaluate the performance of such a funding program in terms of three aspects: output, outcome and impact under given funding inputs, R&D environment of a recipient company, and external evaluation programs of funding organization. We adopt Malcolm Baldrige National Quality Award (MBNQA) criteria to assess the R&D environmental factors of recipient companies. In addition, we test the effect of interim evaluation of the funded project. The proposed model is applied to the real case and is used to identify the best practices as well as to provide feedback information for the improvement of the government funding programs of the R&D projects of SMEs.

RNA Microarray analysis of channels and transporters in normal and fetal Down Syndrome (trisomy 21) brain

A couple of transporters and channels has been proposed as candidate genes involved in the pathomechanisms leading to the neurodevelopmental abnormalities and the phenotype of Down Syndrome (DS, trisomy 21). No systematic study, however, has been carried out showing the concomitant expression of several candidate RNAs during fetal life. It was therefore the aim of the study to apply an array of 96 brain RNAs mainly consisting of channels and transporters to show their expressional levels in fetal DS brain at the early second trimester. Brain RNA was extracted from fetal cortex of the 18-19th week of gestation of controls and DS individuals and used for the GEArray Q Series Human Neuroscience-1/Ion Channels & Transporters analysis. 15 out of 96 RNAs of the array were observed on the films in both groups during this gestational period consisting of genes for potassium, sodium, calcium channels and transporters (ASIC3, ATP1B1, CACNA1B, KCNB2, KCNC1, KCND2, KCNF1, KCNN1, KCNN3, hKCa4, KCNQ2, lipid transfer protein II, SCN2B, acetyl choline transporter, glutamate transporter3). There was no statistically significant difference between the control and the DS group. We provide information on the developmental expression of the aforementioned 15 RNAs and the absence of the residual examined 81 RNAs at the 18th/19th week of gestation in fetal cortex that was never reported before and show that channels and transporters present with unchanged expression in fetal DS brain.

Transcription factor REST dependent proteins are comparable between Down Syndrome and control brains: challenging a hypothesis

Impairment of the RE-1-silencing transcription factor (REST) and REST-dependent genes in Down Syndrome (DS) neuronal progenitor cells and neurospheres has been published recently. As dysregulation of this system has been shown at the RNA level and considering the long and unpredictable way from RNA to proteins, and as it is the proteins that do the function in brain, we decided to test this hypothesis at the protein level. Cortex of brains of patients with Down Syndrome at the early second trimester were used. REST-dependent structures as synapsin I, brain derived neurotrophic factor BDNF and neuronal growth-associated protein SCG10 were determined at the protein level using immunoblotting. Proteins were comparably expressed in fetal Down syndrome and control brains. Even when normalized versus housekeeping genes (glyceraldehyde-6-phosphate-dehydrogenease) and a marker for neuronal density (neuron-specific enolase) DS results were resembling controls. Therefore, we cannot confirm the REST-hypothesis by our studies in the 18/19th week of gestation at the protein level in brain and taking into account that the hypothesis was based upon studies in progenitor cells.

Pattern recognition for road traffic accident severity in Korea

An increasing number of road traffic accidents (RTA) in Korea has emerged as being harmful both for the economy and for safety. An accurately estimated classification model for several severity types of RTA as a function of related factors provides crucial information for the prevention of potential accidents. Here, three data-mining techniques (neural network, logistic regression, decision tree) are used to select a set of influential factors and to build up classification models for accident severity. The three approaches are then compared in terms of classification accuracy. The finding is that accuracy does not differ significantly for each model and that the protective device is the most important factor in the accident severity variation.

Quality function deployment applied to local traffic accident reduction

One of the major tasks of police stations is the management of local road traffic accidents. Proper prevention policy which reflects the local accident characteristics could immensely help individual police stations in decreasing various severity levels of road traffic accidents. In order to relate accident variation to local driving environmental characteristics, we use both cluster analysis and Poisson regression. The fitted result at the level of each cluster for each type of accident severity is utilized as an input to quality function deployment. Quality function deployment (QFD) has been applied to customer satisfaction in various industrial quality improvement settings, where several types of customer requirements are related to various control factors. We show how QFD enables one to set priorities on various road accident control policies to which each police station has to pay particular attention.

A comparative study for stepwise correlated binary regression

Real-time monitored binary data are often recorded along with a large amount of associated covariates for biomedical image processing. Serially measured binary outcomes and covariates could be autocorrelated. Appropriate variable selection schemes are necessary to find a set of influential covariates on the changes in the correlated binary outcomes. Selected variables can be used as feedback information to reduce the dimension of the database. In this context, we examine the performance of the stepwise correlated binary regression. Several realistic situations of the real-time monitored binary data are considered in Monte-Carlo simulation. Results of a simulation study are discussed.

Meta-analysis on total braking time

The total braking time (TBT) distribution is used as an input to two important traffic safety parameters: minimum following gap and stopping sight distance. It is therefore important to accurately estimate the TBT distribution. However, the previously published results on TBT distribution vary widely and confuse practitioners. In this paper, a meta-analysis is used in an effort to investigate the sources of variation in the studies of TBT. According to the results of the meta-analysis, significant characteristics of the mean of total braking time are the awareness level of the driver and the country in which the experiment took place. In addition to these two characteristics, both the type of brake stimulus and the distance away from the brake stimulus are found to be influential characteristics on the variance of total braking time. Based on several combinations of these factors, TBT distributions are reconstructed. It is recommended that the percentile estimates of TBT used for the minimum following gap and stopping sight distance need to be adjusted.