Glycemic Control and Urinary Tract Infections in Women with Type 1 Diabetes: Results from the DCCT/EDIC

PurposeWe examined the relationship between glycemic control and urinary tract infections in women with type 1 diabetes mellitus.Materials and methodsWomen enrolled in the Epidemiology of Diabetes Interventions and Complications study, the observational followup of the Diabetes Control and Complications Trial, were surveyed to assess the rate of physician diagnosed urinary tract infections in the preceding 12 months. The relationship between glycated hemoglobin levels and number of urinary tract infections in the previous 12 months was assessed using a multivariable Poisson regression model.ResultsA total of 572 women were evaluated at year 17. Mean age was 50.7 ± 7.2 years, mean body mass index was 28.6 ± 5.9 kg/m(2), mean type 1 diabetes duration was 29.8 ± 5.0 years and mean glycated hemoglobin was 8.0% ± 0.9%. Of these women 86 (15.0%) reported at least 1 physician diagnosed urinary tract infection during the last 12 months. Higher glycated hemoglobin levels were significantly associated with number of urinary tract infections such that for every unit increase (1%) in recent glycated hemoglobin level, there was a 21% (p=0.02) increase in urinary tract infection frequency in the previous 12 months after adjusting for race, hysterectomy status, urinary incontinence, sexual activity in the last 12 months, peripheral and autonomic neuropathy, and nephropathy.ConclusionsThe frequency of urinary tract infections increases with poor glycemic control in women with type 1 diabetes. This relationship is independent of other well described predictors of urinary tract infections and suggests that factors directly related to glycemic control may influence the risk of lower urinary tract infections

Epigenomic profiling reveals an association between persistence of DNA methylation and metabolic memory in the DCCT/EDIC type 1 diabetes cohort

We examined whether persistence of epigenetic DNA methylation (DNA-me) alterations at specific loci over two different time points in people with diabetes are associated with metabolic memory, the prolonged beneficial effects of intensive vs. conventional therapy during the Diabetes Control and Complications Trial (DCCT) on the progression of microvascular outcomes in the long-term follow-up Epidemiology of Diabetes Interventions and Complications (EDIC) Study. We compared DNA-me profiles in genomic DNA of whole blood (WB) isolated at EDIC Study baseline from 32 cases (DCCT conventional therapy group subjects showing retinopathy or albuminuria progression by EDIC Study year 10) vs. 31 controls (DCCT intensive therapy group subjects without complication progression by EDIC year 10). DNA-me was also profiled in blood monocytes (Monos) of the same patients obtained during EDIC Study years 16-17. In WB, 153 loci depicted hypomethylation, and 225 depicted hypermethylation, whereas in Monos, 155 hypomethylated loci and 247 hypermethylated loci were found (fold change ≥1.3; P < 0.005; cases vs. controls). Twelve annotated differentially methylated loci were common in both WB and Monos, including thioredoxin-interacting protein (TXNIP), known to be associated with hyperglycemia and related complications. A set of differentially methylated loci depicted similar trends of associations with prior HbA1c in both WB and Monos. In vitro, high glucose induced similar persistent hypomethylation at TXNIP in cultured THP1 Monos. These results show that DNA-me differences during the DCCT persist at certain loci associated with glycemia for several years during the EDIC Study and support an epigenetic explanation for metabolic memory

Genetic variation in the Estonian population : pharmacogenomics study of adverse drug effects using electronic health records

Pharmacogenomics aims to tailor pharmacological treatment to each individual by considering associations between genetic polymorphisms and adverse drug effects (ADEs). With technological advances, pharmacogenomic research has evolved from candidate gene analyses to genome-wide association studies. Here, we integrate deep whole-genome sequencing (WGS) information with drug prescription and ADE data from Estonian electronic health record (EHR) databases to evaluate genome- and pharmacome-wide associations on an unprecedented scale. We leveraged WGS data of 2240 Estonian Biobank participants and imputed all single-nucleotide variants (SNVs) with allele counts over 2 for 13,986 genotyped participants. Overall, we identified 41 (10 novel) loss-of-function and 567 (134 novel) missense variants in 64 very important pharmacogenes. The majority of the detected variants were very rare with frequencies below 0.05%, and 6 of the novel lossof-function and 99 of the missense variants were only detected as single alleles (allele count = 1). We also validated documented pharmacogenetic associations and detected new independent variants in known gene-drug pairs. Specifically, we found that CTNNA3 was associated with myositis and myopathies among individuals taking nonsteroidal anti-inflammatory oxicams and replicated this finding in an extended cohort of 706 individuals. These findings illustrate that population-based WGS-coupled EHRs are a useful tool for biomarker discovery