A Gene Variant in CERS2 Is Associated with Rate of Increase in Albuminuria in Patients with Diabetes from ONTARGET and TRANSCEND

<div><p>Although albuminuria and subsequent advanced stage chronic kidney disease are common among patients with diabetes, the rate of increase in albuminuria varies among patients. Since genetic variants associated with estimated glomerular filtration rate (eGFR) were identified in cross sectional studies, we asked whether these variants were also associated with rate of increase in albuminuria among patients with diabetes from ONTARGET and TRANSCEND—randomized controlled trials of ramipril, telmisartan, both, or placebo. For 16 genetic variants associated with eGFR at a genome-wide level, we evaluated the association with annual rate of increase in albuminuria estimated from urine albumin:creatinine ratio (uACR). One of the variants (rs267734) was associated with rate of increase in albuminuria. The annual rate of increase in albuminuria among risk homozygotes (69% of the study population) was 11.3% (95%CI; 7.5% to 15.3%), compared with 5.0% (95%CI; 3.3% to 6.8%) for heterozygotes (27% of the population), and 1.7% (95%CI; −1.7% to 5.3%) for non-risk homozygotes (4% of the population); P = 0.0015 for the difference between annual rates in the three genotype groups. These estimates were adjusted for age, sex, ethnicity, and principal component of genetic heterogeneity. Among patients without albuminuria at baseline (uACR<30 mg/g), each risk allele was associated with 50% increased risk of incident albuminuria (OR = 1.50; 95%CI 1.15 to 1.95; P = 0.003) after further adjustment for traditional risk factors including baseline uACR and eGFR. The rs267734 variant is in almost perfect linkage-disequilibrium (r² = 0.94) with rs267738, a single nucleotide polymorphism encoding a glutamic acid to alanine change at position 115 of the ceramide synthase 2 (CERS2) encoded protein. However, it is unknown whether CERS2 function influences albuminuria. In conclusion, we found that rs267734 in CERS2 is associated with rate of increase in albuminuria among patients with diabetes and elevated risk of cardiovascular disease.</p></div

SNPs associated with eGFR in cross-sectional studies.

<p>LD, linkage disequilibrium.</p><p>SNPs associated with eGFR in cross-sectional studies.</p

Association between SNPs and change in albuminuria.

<p>Results were adjusted for age, sex, 10 largest principal components of genetic variation and self-reported ethnicity (for “all ethnic groups” results).</p><p>Association between SNPs and change in albuminuria.</p

Regional plot for the CERS2 locus.

<p>SNPs are plotted by association P value of linear mixed models adjusted for age, sex, and principal components of genetic variation for the association between SNP and annual rate of change in albuminuria. and genomic position (NCBI Build 36). The original hit (rs267734) is labeled. The magnitude of linkage disequilibrium (r²) between each SNP and rs267734 is indicated by the intensity of the red coloring. Estimates of recombination rates are shown by the blue line. Gene positions are indicated by green arrows. Gene names are labeled. Linkage disequilibrium and recombination rates were estimated from the Utah residents of Northern and Western European ancestry (CEU) HapMap population (release 22). Plots were prepared using SNAP <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106631#pone.0106631-Laviad1" target="_blank">[22]</a>. Panel A: P values adjusted for rs267734. Panel B: P values not adjusted for rs267734.</p

Association between SNPs and baseline albuminuria.

<p>β is the expected change in log-transformed baseline albuminuria for each risk allele. Models are adjusted for age, sex, 10 largest principal components of genetic variation, and self-reported ethnicity.</p><p>Association between SNPs and baseline albuminuria.</p

Baseline characteristics.

<p>SD, standard deviation. IQR, inter-quartile range. NA, not applicable.</p><p>Baseline characteristics.</p

Association between SNPs and baseline eGFR.

<p>β is the expected change in log-transformed baseline eGFR for each risk allele. Models are adjusted for age, sex, 10 largest principal components of genetic variation, and self-reported ethnicity.</p><p>Association between SNPs and baseline eGFR.</p

A Robust Method for Iodine Status Determination in Epidemiological Studies by Capillary Electrophoresis

Iodine deficiency is the most common preventable cause of intellectual disabilities in children. Global health initiatives to ensure optimum nutrition thus require continuous monitoring of population-wide iodine intake as determined by urinary excretion of iodide. Current methods to analyze urinary iodide are limited by complicated sample pretreatment, costly infrastructure, and/or poor selectivity, posing restrictions to large-scale epidemiological studies. We describe a simple yet selective method to analyze iodide in volume-restricted human urine specimens stored in biorepositories by capillary electrophoresis (CE) with UV detection. Excellent selectivity is achieved when using an acidic background electrolyte in conjunction with dynamic complexation via α-cyclodextrin in an unmodified fused-silica capillary under reversed polarity. Sample self-stacking is developed as a novel online sample preconcentration method to boost sensitivity with submicromolar detection limits for iodide (S/N ≈ 3, 0.06 μM) directly in urine. This assay also allows for simultaneous analysis of environmental iodide uptake inhibitors, including thiocyanate and nitrate. Rigorous method validation confirmed good linearity (<i>R</i>² = 0.9998), dynamic range (0.20 to 4.0 μM), accuracy (average recovery of 93% at three concentration levels) and precision for reliable iodide determination in pooled urine specimens over 29 days of analysis (RSD = 11%, <i>n</i> = 87)

Maternal and Pregnancy Related Predictors of Cardiometabolic Traits in Newborns

<div><p>Background</p><p>The influence of multiple maternal and pregnancy characteristics on offspring cardiometabolic traits at birth is not well understood and was evaluated in this study.</p><p>Methods and Findings</p><p>The Family Atherosclerosis Monitoring In earLY life (FAMILY) Study prospectively evaluated 11 cardiometabolic traits in 901 babies born to 857 mothers. The influence of maternal age, health (pre-pregnancy weight, blood pressure, glycemic status, lipids), health behaviors (diet, activity, smoking) and pregnancy characteristics (gestational age at birth, gestational weight gain and placental-fetal ratio) were examined. Greater gestational age influenced multiple newborn cardiometabolic traits including cord blood lipids, glucose and insulin, body fat and blood pressure. In a subset of 442 singleton mother/infant pairs, principal component analysis grouped 11 newborn cardiometabolic traits into 5 components (anthropometry/insulin, 2 lipid components, blood pressure and glycemia), accounting for 74% of the variance of the 11 outcome variables. Determinants of these components, corrected for sex and gestational age, were examined. Baby anthropometry/insulin was independently predicted by higher maternal pre-pregnancy weight (standardized estimate 0.30) and gestational weight gain (0.30; both p<0.0001) and was inversely related to smoking during pregnancy (−0.144; p = 0.01) and maternal polyunsaturated to saturated fat intake (−0.135;p = 0.01). Component 2 (HDL-C/Apo Apolipoprotein1) was inversely associated with maternal age. Component 3 (blood pressure) was not clustered with any other newborn cardiometabolic trait and no associations with maternal pregnancy characteristics were identified. Component 4 (triglycerides) was positively associated with maternal hypertension and triglycerides, and inversely associated with maternal HDL and age. Component 5 (glycemia) was inversely associated with placental/fetal ratio (−0.141; p = 0.005). LDL-C was a bridging variable between the lipid factors and glycemia.</p><p>Conclusions</p><p>Maternal health, health behaviours and placenta to fetal weight ratio are associated with newborn cardiometabolic traits over and above gestational age. Future investigations are needed to determine if these factors remain important determinants of cardiometabolic health throughout childhood.</p></div

Newborn cardiometablic traits by gestational age at birth [mean (SE)].

<p>Newborn cardiometablic traits by gestational age at birth [mean (SE)].</p