The combination of mitochondrial low enzyme-activity aldehyde dehydrogenase 2 allele and superoxide dismutase 2 genotypes increases the risk of hypertension in relation to alcohol consumption.

A cooperative role of mitochondrial aldehyde dehydrogenase 2 (ALDH2) and superoxide dismutase 2 (SOD2) to maintain the vascular function has recently been demonstrated in nitrate tolerance. The present study examined whether the combination of low enzyme-activity variants of ALDH2 and SOD2 increases the risk of hypertension in relation to alcohol consumption. A total of 444 Japanese participants in a health-screening program were evaluated. The risk of hypertension among the individuals harboring both the ALDH2*2 allele and the SOD2 Val/Val genotype was significantly higher in drinkers than in nondrinkers (adjusted odds ratio, 6.22; 95% confidence interval, 2.26-17.1; P<0.001). Among these individuals, the systolic/diastolic blood pressure also increased by 0.24/0.14 mmHg for each 1g/day increase in alcohol consumption (P<0.001/P=0.003). These associations were observed, but the degree was lower among those with the other genotype combinations (0.11/0.10 mmHg; P=0.012/P=0.001). Information about the genetic predisposition to alcohol-related diseases may thus be useful to promote lifestyle modifications for high-risk individuals.

Association between combinations of glutathione-S-transferase M1, T1 and P1 genotypes and non-alcoholic fatty liver disease.

BACKGROUND/AIMS: Glutathione-S-transferases (GSTs) play a crucial role in antioxidant defence mechanisms, by detoxifying xenobiotics and by inactivating endogenous byproducts of oxidative stress. Functional failure, as a sequel of an altered GST genotype, may thus aggravate non-alcoholic fatty liver disease (NAFLD). This study investigated whether the GSTs genotypes could affect the risk for NAFLD. METHODS: A cross-sectional case-control analysis included 253 Japanese participants in a health screening programme. The GSTM1 null, GSTT1 null and GSTP1 Ile105Val variant genotypes were determined as putative high-risk genotypes. RESULTS: The incidence of NAFLD was 27.3%. The frequency of the GSTM1 null genotype was higher in NAFLD than in the control [adjusted odds ratio (OR), 2.00; 95% confidence intervals (CI), 1.01-3.95]. Moreover, any combination of two putative high-risk genotypes exhibited a higher risk for NAFLD with an adjusted OR from 3.52 (95% CI, 1.08-11.43)-4.01 (95% CI, 1.28-12.56). However, the significance for the combination of GSTM1 null and GSTT1 null genotypes only remained after Bonferroni's correction. In addition, the risk for NAFLD increased as the number of high-risk genotypes, and the OR among three high-risk genotypes carriers was 9.67 (95% CI: 1.61-58.26). CONCLUSION: This is the first report to show the impact of the GSTs genotypes on the development of NAFLD. This finding, which should be confirmed in further studies in larger populations, may help to develop a more targeted prevention programme at an early stage for subjects with an increased risk for NAFLD.

Association between SCN1A polymorphism and carbamazepine-resistant epilepsy.

AIMS: To establish whether the SCN1A IVS5-91 G > A polymorphism of the SCN1A gene, which encodes the neuronal sodium channel alpha subunit, affects responsiveness to the antiepileptic drugs (AEDS) carbamazepine and/or phenytoin. METHODS: SCN1A IVS5-91 G > A polymorphism was genotyped in 228 Japanese epileptic patients treated with AEDs. The association between AED responsiveness and the polymorphism was estimated by logistic regression analysis, adjusting for clinical factors affecting the outcome of AED therapy. RESULTS: The frequency of the AA genotype was significantly higher in carbamazepine-resistant patients (odds ratio, 2.7; 95% confidence interval (CI), 1.1, 7.1) and was insignificantly higher in AED-resistant patients. CONCLUSIONS: This is the first report demonstrating an association between the SCN1A polymorphism and carbamazepine-resistant epilepsy.

A preliminary investigation of the association between haptoglobin polymorphism, serum ferritin concentration and fatty liver disease.

BACKGROUND: The genetic polymorphism of haptoglobin (HP) has been associated with cardiovascular disease and type 2 diabetes. We hypothesized that the HP polymorphism could affect the incidence of nonalcoholic fatty liver disease (NAFLD). METHODS: This cross sectional case-control analysis included 337 Japanese participants in a health screening program. Fatty liver disease (FLD) was diagnosed by ultrasonography scanning and was classified into NAFLD based on the daily alcohol intake. The HP1 and HP2 alleles were determined using the PCR, and serum ferretin concentrations were measured. RESULTS: FLD and NAFLD were diagnosed in 91 and 69 subjects, respectively. The adjusted odd ratio (OR) of HP2 carriers vs. non-carriers was 11.8 [95% confidence intervals (CI), 1.3-104.0] for FLD, and 11.7 (95% CI, 1.3-107.9) for NAFLD. Male FLD cases with the HP2/HP2genotype had significantly higher ferretin concentrations than those without (P=0.003). The ferritin concentrations were correlated with the alanine-aminotransferase activities (r=0.48, P<0.001 in FLD cases with the HP2/HP2 genotype). Ferritin was an independent risk factor for FLD, and the incidence of FLD significantly increased in association with ferritin. CONCLUSIONS: This is a preliminary but the first report suggesting the HP2 allele to be a candidate risk factor for NAFLD.

Multi-objective genetic algorithm-based sample selection for partial least squares model building with applications to near-infrared spectroscopic data.

In this study, multi-objective genetic algorithms (GAs) are introduced to partial least squares (PLS) model building. This method aims to improve the performance and robustness of the PLS model by removing samples with systematic errors, including outliers, from the original data. Multi-objective GA optimizes the combination of these samples to be removed. Training and validation sets were used to reduce the undesirable effects of over-fitting on the training set by multi-objective GA. The reduction of the over-fitting leads to accurate and robust PLS models. To clearly visualize the factors of the systematic errors, an index defined with the original PLS model and a specific Pareto-optimal solution is also introduced. This method is applied to three kinds of near-infrared (NIR) spectra to build PLS models. The results demonstrate that multi-objective GA significantly improves the performance of the PLS models. They also show that the sample selection by multi-objective GA enhances the ability of the PLS models to detect samples with systematic errors.

New methodology to obtain a calibration model for noninvasive near-infrared blood glucose monitoring.

This paper reports new methodology to obtain a calibration model for noninvasive blood glucose monitoring using diffuse reflectance near-infrared (NIR) spectroscopy. Conventional studies of noninvasive blood glucose monitoring with NIR spectroscopy use a calibration model developed by in vivo experimental data sets. In order to create a calibration model, we have used a numerical simulation of light propagation in skin tissue to obtain simulated NIR diffuse reflectance spectra. The numerical simulation method enables us to design parameters affecting the prediction of blood glucose levels and their variation ranges for a data set to create a calibration model using multivariate analysis without any in vivo experiments in advance. By designing the parameters and their variation ranges appropriately, we can prevent a calibration model from chance temporal correlations that are often observed in conventional studies using NIR spectroscopy. The calibration model (regression coefficient vector) obtained by the numerical simulation has a characteristic positive peak at the wavelength around 1600 nm. This characteristic feature of the regression coefficient vector is very similar to those obtained by our previous in vitro and in vivo experimental studies. This positive peak at around 1600 nm also corresponds to the characteristic absorption band of glucose. The present study has reinforced that the characteristic absorbance of glucose at around 1600 nm is useful to predict the blood glucose level by diffuse reflectance NIR spectroscopy. We have validated this new calibration methodology using in vivo experiments. As a result, we obtained a coefficient of determination, r2, of 0.87 and a standard error of prediction (SEP) of 12.3 mg/dL between the predicted blood glucose levels and the reference blood glucose levels for all the experiments we have conducted. These results of in vivo experiments indicate that if the parameters and their vibration ranges are appropriately taken into account in a numerical simulation, the new calibration methodology provides us with a very good calibration model that can predict blood glucose levels with small errors without conducting any experiments in advance to create a calibration model for each individual patient. This new calibration methodology using numerical simulation has promising potential for NIR spectroscopy, especially for noninvasive blood glucose monitoring.

Noninvasive near-infrared blood glucose monitoring using a calibration model built by a numerical simulation method: Trial application to patients in an intensive care unit.

We have applied a new methodology for noninvasive continuous blood glucose monitoring, proposed in our previous paper, to patients in ICU (intensive care unit), where strict controls of blood glucose levels are required. The new methodology can build calibration models essentially from numerical simulation, while the conventional methodology requires pre-experiments such as sugar tolerance tests, which are impossible to perform on ICU patients in most cases. The in vivo experiments in this study consisted of two stages, the first stage conducted on healthy subjects as preliminary experiments, and the second stage on ICU patients. The prediction performance of the first stage was obtained as a correlation coefficient (r) of 0.71 and standard error of prediction (SEP) of 28.7 mg/dL. Of the 323 total data, 71.5% were in the A zone, 28.5% were in the B zone, and none were in the C, D, and E zones for the Clarke error-grid analysis. The prediction performance of the second stage was obtained as an r of 0.97 and SEP of 27.2 mg/dL. Of the 304 total data, 80.3% were in the A zone, 19.7% were in the B zone, and none were in the C, D, and E zones. These prediction results suggest that the new methodology has the potential to realize a noninvasive blood glucose monitoring system using near-infrared spectroscopy (NIRS) in ICUs. Although the total performance of the present monitoring system has not yet reached a satisfactory level as a stand-alone system, it can be developed as a complementary system to the conventional one used in ICUs for routine blood glucose management, which checks the blood glucose levels of patients every few hours.

Improvement of the partial least squares model performance for oral glucose intake experiments by inside mean centering and inside multiplicative signal correction.

Near infrared (NIR) spectroscopy has become a promising technique for the in vivo monitoring of glucose. Several capillary-rich locations in the body, such as the tongue, forearm, and finger, have been used to collect the in vivo spectra of blood glucose. For such an in vivo determination of blood glucose, collected NIR spectra often show some dependence on the measurement conditions and human body features at the location on which a probe touches. If NIR spectra collected for different oral glucose intake experiments, in which the skin of different patients and the measurement conditions may be quite different, are directly used, partial least squares (PLS) models built by using them would often show a large prediction error because of the differences in the skin of patients and the measurement conditions. In the present study, the NIR spectra in the range of 1300-1900 nm were measured by conveniently touching an optical fiber probe on the forearm skin with a system that was developed for in vivo measurements in our previous work. The spectra were calibrated to resolve the problem derived from the difference of patient skin and the measurement conditions by two proposed methods, inside mean centering and inside multiplicative signal correction (MSC). These two methods are different from the normal mean centering and normal multiplicative signal correction (MSC) that are usually performed to spectra in the calibration set, while inside mean centering and inside MSC are performed to the spectra in every oral glucose intake experiment. With this procedure, spectral variations resulted from the measurement conditions, and human body features will be reduced significantly. More than 3000 NIR spectra were collected during 68 oral glucose intake experiments, and calibrated. The development of PLS calibration models using the spectra show that the prediction errors can be greatly reduced. This is a potential chemometric technique with simplicity, rapidity and efficiency in the pretreatment of NIR spectra collected during oral glucose intake experiments.