Anthropomorphic Measurements That Include Central Fat Distribution Are More Closely Related with Key Risk Factors than BMI in CKD Stage 3

Background: Body Mass Index (BMI) as a marker of obesity is an established risk factor for chronic kidney disease (CKD) and cardiovascular disease (CVD). However, BMI can overestimate obesity. Anthropomorphic measurements that include central fat deposition are emerging as a more important risk factor. We studied BMI, waist circumference (WC), waist-to-height ratio (WHtR), waist-to-hip ratio (WHR) and conicity index (CI) in a cohort of patients with CKD stage 3 and compared the associations with other known risk factors for CKD progression and CVD. Methods: 1740 patients with CKD stage 3 were recruited from primary care for the Renal Risk in Derby study. Each participant underwent clinical assessment, including anthropomorphic measurements and pulse wave velocity (PWV), as well as urine and serum biochemistry tests. Results: The mean age of the cohort was 72.969 years with 60 % females. The mean eGFR was 52.5610.4 ml/min/1.73 m 2 and 16.9 % of the cohort had diabetes. With the cohort divided into normal and increased risk of morbidity and mortality using each anthropomorphic measurement, those measurements that included increased central fat distribution were significantly associated with more risk factors for CKD progression and CVD than increased BMI. Univariable analysis demonstrated central fat distribution was correlated with more risk factors than BMI. Subgroup analyses using recognised BMI cut-offs to define obesity and quartiles of WHR and CI demonstrated that increasing central fat distribution wa

A Single Nucleotide Polymorphism within the Acetyl-Coenzyme A Carboxylase Beta Gene Is Associated with Proteinuria in Patients with Type 2 Diabetes

It has been suggested that genetic susceptibility plays an important role in the pathogenesis of diabetic nephropathy. A large-scale genotyping analysis of gene-based single nucleotide polymorphisms (SNPs) in Japanese patients with type 2 diabetes identified the gene encoding acetyl-coenzyme A carboxylase beta (ACACB) as a candidate for a susceptibility to diabetic nephropathy; the landmark SNP was found in the intron 18 of ACACB (rs2268388: intron 18 +4139 C > T, p = 1.4×10−6, odds ratio = 1.61, 95% confidence interval [CI]: 1.33–1.96). The association of this SNP with diabetic nephropathy was examined in 9 independent studies (4 from Japan including the original study, one Singaporean, one Korean, and two European) with type 2 diabetes. One case-control study involving European patients with type 1 diabetes was included. The frequency of the T allele for SNP rs2268388 was consistently higher among patients with type 2 diabetes and proteinuria. A meta-analysis revealed that rs2268388 was significantly associated with proteinuria in Japanese patients with type 2 diabetes (p = 5.35×10−8, odds ratio = 1.61, 95% Cl: 1.35–1.91). Rs2268388 was also associated with type 2 diabetes–associated end-stage renal disease (ESRD) in European Americans (p = 6×10−4, odds ratio = 1.61, 95% Cl: 1.22–2.13). Significant association was not detected between this SNP and nephropathy in those with type 1 diabetes. A subsequent in vitro functional analysis revealed that a 29-bp DNA fragment, including rs2268388, had significant enhancer activity in cultured human renal proximal tubular epithelial cells. Fragments corresponding to the disease susceptibility allele (T) had higher enhancer activity than those of the major allele. These results suggest that ACACB is a strong candidate for conferring susceptibility for proteinuria in patients with type 2 diabetes

Potential impact of renin-angiotensin system inhibitors and calcium channel blockers on plasma high-molecular-weight adiponectin levels in hemodialysis patients

著者最終原稿版Although metabolic syndrome confers an increased risk of cardiovascular disease in the general population, little is known about the alteration of abdominal adiposity and its association with adipocytokines in hemodialysis patients. We investigated the plasma high-molecular-weight (HMW) adiponectin level and its relationship to visceral fat area (VFA) and various markers of atherosclerosis in hemodialysis patients. In a cross-sectional study, conventional cardiovascular risk factors, plasma total and HMW adiponectin, the number of components of the metabolic syndrome and, using computed tomography, the distribution of abdominal adiposity were assessed in 144 hemodialysis patients (90 men and 54 women; mean age, 60.7 years) and 30 age- and sex-matched patients with chronic kidney disease (CKD). Plasma HMW adiponectin levels in hemodialysis patients were significantly higher than those in patients with CKD, negatively associated with VFA and serum triglycerides and positively associated with plasma total adiponectin, as well as the HMW-to-total adiponectin ratio in men and women (all P < 0.05) in a simple regression analysis. In a multiple regression analysis, VFA was a significant determinant of HMW adiponectin in hemodialysis patients. Furthermore, after adjustment for classical risk factors, HMW adiponectin levels were significantly higher in patients undergoing treatment with renin-angiotensin system inhibitors or calcium channel blockers compared with patients not undergoing such treatment. This study shows that plasma HMW adiponectin levels were negatively associated with VFA and positively associated with treatment with blockade of the renin-angiotensin system and of the calcium channel. Therefore, these drugs might be effective for improving adipocytokine-related metabolic abnormalities in hemodialysis patients

Bayesian Approach To Wavelet Decomposition and Shrinkage

We consider Bayesian approach to wavelet decomposition. We show how prior knowledge about a function&apos;s regularity can be incorporated into a prior model for its wavelet coefficients by establishing a relationship between the hyperparameters of the proposed model and the parameters of those Besov spaces within which realizations from the prior will fall. Such a relation may be seen as giving insight into the meaning of the Besov space parameters themselves. Furthermore, we consider Bayesian wavelet-based function estimation that gives rise to different types of wavelet shrinkage in non-parametric regression. Finally, we discuss an extension of the proposed Bayesian model by considering random functions generated by an overcomplete wavelet dictionary. 1 Introduction Consider the standard non-parametric regression problem: y i = g(t i ) + ffl i ; i = 1; : : : ; n; (1.1) and suppose we wish to recover the unknown function g from additive noise ffl i given noisy data y i at discrete point..