Carolina Soriano-Tárraga

Epigenome-wide association study identifies TXNIP gene associated with type 2 diabetes mellitus and sustained hyperglycemia

Type 2 diabetes mellitus (DM) is an established risk factor for a wide range of vascular diseases, including ischemic stroke (IS). Glycated hemoglobin A1c (HbA1c), a marker for average blood glucose levels over the previous 12 weeks, is used as a measure of glycemic control and also as a diagnostic criterion for diabetes (HbA1c levels ≥ 6.5%). Epigenetic mechanisms, such as DNA methylation, may be associated with aging processes and with modulation of the risk of various pathologies, such as DM. Specifically, DNA methylation could be one of the mechanisms mediating the relation between DM and environmental exposures. Our goal was to identify new CpG methylation sites associated with DM. We performed a genome-wide methylation study in whole-blood DNA from an IS patient cohorts. Illumina HumanMethylation450 BeadChip array was used to measure DNA methylation in CpG sites. All statistical analyses were adjusted for sex, age, hyperlipidemia, body mass index (BMI), smoking habit and cell count. Findings were replicated in two independent cohorts, an IS cohort and a population-based cohort, using the same array. In the discovery phase (N = 355), we identified a CpG site, cg19693031 (located in the TXNIP gene) that was associated with DM (P = 1.17 × 10(-12)); this CpG was replicated in two independent cohorts (N = 167 and N = 645). Methylation of TXNIP was inversely and intensely associated with HbA1c levels (P = 7.3 × 10(-16)), specifically related to diabetic patients with poor control of glucose levels. We identified an association between the TXNIP gene and DM through epigenetic mechanisms, related to sustained hyperglycemia levels (HbA1c ≥ 7%).

Identification and validation of seven new loci showing differential DNA methylation related to serum lipid profile: an epigenome-wide approach. The REGICOR study.

Lipid traits (total, low-density and high-density lipoprotein cholesterol, and triglycerides) are risk factors for cardiovascular disease. DNA methylation is not only an inherited but also modifiable epigenetic mark that has been related to cardiovascular risk factors. Our aim was to identify loci showing differential DNA methylation related to serum lipid levels. Blood DNA methylation was assessed using the Illumina Human Methylation 450 BeadChip. A two-stage epigenome-wide association study was performed, with a discovery sample in the REGICOR study (n = 645) and validation in the Framingham Offspring Study (n = 2,542). Fourteen CpG sites located in nine genes (SREBF1, SREBF2, PHOSPHO1, SYNGAP1, ABCG1, CPT1A, MYLIP, TXNIP and SLC7A11) and 2 intergenic regions showed differential methylation in association with lipid traits. Six of these genes and 1 intergenic region were new discoveries showing differential methylation related to total cholesterol (SREBF2), HDL-cholesterol (PHOSPHO1, SYNGAP1 and an intergenic region in chromosome 2) and triglycerides (MYLIP, TXNIP and SLC7A11). These CpGs explained 0.7%, 9.5% and 18.9% of the variability of total cholesterol, HDL cholesterol and triglycerides in the Framingham Offspring Study, respectively. The expression of the genes SREBF2 and SREBF1 was inversely associated with methylation of their corresponding CpGs (P-value = 0.0042 and 0.0045, respectively) in participants of the GOLDN study (n = 98). In turn, SREBF1 expression was directly associated with HDL cholesterol (P-value = 0.0429). Genetic variants in SREBF1, PHOSPHO1, ABCG1 and CPT1A were also associated with lipid profile. Further research is warranted to functionally validate these new loci and assess the causality of new and established associations between these differentially methylated loci and lipid metabolism.

Global DNA Methylation of Ischemic Stroke Subtypes

Ischemic stroke (IS), a heterogeneous multifactorial disorder, is among the leading causes of mortality and long-term disability in the western world. Epidemiological data provides evidence for a genetic component to the disease, but its epigenetic involvement is still largely unknown. Epigenetic mechanisms, such as DNA methylation, change over time and may be associated with aging processes and with modulation of the risk of various pathologies, such as cardiovascular disease and stroke. We analyzed 2 independent cohorts of IS patients. Global DNA methylation was measured by luminometric methylation assay (LUMA) of DNA blood samples. Univariate and multivariate regression analyses were used to assess the methylation differences between the 3 most common IS subtypes, large-artery atherosclerosis (LAA), small-artery disease (SAD), and cardio-aortic embolism (CE). A total of 485 IS patients from 2 independent hospital cohorts (n = 281 and n = 204) were included, distributed across 3 IS subtypes: LAA (78/281, 59/204), SAD (97/281, 53/204), and CE (106/281, 89/204). In univariate analyses, no statistical differences in LUMA levels were observed between the 3 etiologies in either cohort. Multivariate analysis, adjusted by age, sex, hyperlipidemia, and smoking habit, confirmed the lack of differences in methylation levels between the analyzed IS subtypes in both cohorts. Despite differences in pathogenesis, our results showed no global methylation differences between LAA, SAD, and CE subtypes of IS. Further work is required to establish whether the epigenetic mechanism of methylation might play a role in this complex disease.

DNA Isolation Method Is a Source of Global DNA Methylation Variability Measured with LUMA. Experimental Analysis and a Systematic Review

In DNA methylation, methyl groups are covalently bound to CpG dinucleotides. However, the assumption that methyl groups are not lost during routine DNA extraction has not been empirically tested. To avoid nonbiological associations in DNA methylation studies, it is essential to account for potential batch effect bias in the assessment of this epigenetic mechanism. Our purpose was to determine if the DNA isolation method is an independent source of variability in methylation status. We quantified Global DNA Methylation (GDM) by luminometric methylation assay (LUMA), comparing the results from 3 different DNA isolation methods. In the controlled analysis (n = 9), GDM differed slightly for the same individual depending on extraction method. In the population analysis (n = 580) there were significant differences in GDM between the 3 DNA isolation methods (medians, 78.1%, 76.5% and 75.1%; p<0.001). A systematic review of published data from LUMA GDM studies that specify DNA extraction methods is concordant with our findings. DNA isolation method is a source of GDM variability measured with LUMA. To avoid possible bias, the method used should be reported and taken into account in future DNA methylation studies.

Identification of a new locus and validation of previously reported loci showing differential methylation associated with smoking. The REGICOR study

Smoking increases the risk of many diseases and could act through changes in DNA methylation patterns. The aims of this study were to determine the association between smoking and DNA methylation throughout the genome at cytosine-phosphate-guanine (CpG) site level and genomic regions. A discovery cross-sectional epigenome-wide association study nested in the follow-up of the REGICOR cohort was designed and included 645 individuals. Blood DNA methylation was assessed using the Illumina HumanMethylation450 BeadChip. Smoking status was self-reported using a standardized questionnaire. We identified 66 differentially methylated CpG sites associated with smoking, located in 38 genes. In most of these CpG sites, we observed a trend among those quitting smoking to recover methylation levels typical of never smokers. A CpG site located in a novel smoking-associated gene (cg06394460 in LNX2) was hypomethylated in current smokers. Moreover, we validated two previously reported CpG sites (cg05886626 in THBS1, and cg24838345 in MTSS1) for their potential relation to atherosclerosis and cancer diseases, using several different approaches: CpG site methylation, gene expression, and plasma protein level determinations. Smoking was also associated with higher THBS1 gene expression but with lower levels of thrombospondin-1 in plasma. Finally, we identified differential methylation regions in 13 genes and in four non-coding RNAs. In summary, this study replicated previous findings and identified and validated a new CpG site located in LNX2 associated with smoking.

Biological age is better than chronological as predictor of 3-month outcome in ischemic stroke

Objective: To analyze the effect of age-related DNA methylation changes in multiple cytosine-phosphate-guanine (CpG) sites (biological age [b-age]) on patient outcomes at 3 months after an ischemic stroke. Methods: We included 511 patients with first-ever acute ischemic stroke assessed at Hospital del Mar (Barcelona, Spain) as the discovery cohort. Demographic and clinical data, including chronological age (c-age), vascular risk factors, initial stroke severity, recanalization treatment, and previous and 3-month modified Rankin Scale (p-mRS and 3-mRS, respectively) were registered. B-age was estimated with an algorithm, based on DNA methylation in 71 CpGs. Bivariate analysis determined variables associated with 3-mRS for inclusion in ordinal multivariate analysis. Results: After ordinal regressions for 3-month ischemic stroke outcome (3-mRS), b-age was associated with outcome (odds ratio 1.04 [95% confidence interval 1.01–1.07]), nullifying c-age. Stepwise regression kept b-age, basal NIH Stroke Scale, sex, p-mRS, and recanalization treatment as better explanatory variables, instead of c-age. These results were successfully replicated in an independent cohort. Conclusions: B-age, estimated by DNA methylation, is an independent predictor of ischemic stroke outcome regardless of chronological years.

Ischemic stroke patients are biologically older than their chronological age

Ischemic stroke is associated with aging. It is possible to predict chronological age by measuring age-related changes in DNA methylation from multiple CpG sites across the genome, known as biological age. The difference between biological age and actual chronological age would indicate an individuals level of aging. Our aim was to determine the biological age of ischemic stroke patients and compare their aging with controls of the same chronological age. A total of 123 individuals, 41 controls and 82 patients with ischemic stroke were paired by chronological age, ranging from 39 to 82 years. Illumina HumanMethylation450 BeadChip array was used to measure DNA methylation in CpG sites in both groups, and biological age was estimated using methylation values of specific CpGs. Ischemic stroke patients were biologically an average 2.5 years older than healthy controls (p-value=0.010). Stratified by age tertiles, younger stroke patients (≤57 years old) were biologically older than controls (OR=1.19; 95%CI 1.00-1.41, p-value=0.046). The older groups showed no biological age differences between cases and controls, but were close to reaching the significance level. Ischemic stroke patients are biologically older than controls. Biological age should be considered as a potential new biomarker of stroke risk.

Dietary Habits in Patients with Ischemic Stroke: A Case-Control Study

Background and Aims Diet appears to have some role in stroke development. The objective of our study was to describe the dietary habits in patients admitted with acute ischemic stroke and compare selected dietary components with healthy controls. Adherence to healthy diet behaviors was also assessed. Methods A case-control study of consecutive patients with acute ischemic stroke admitted to the Neurology Department of Hospital del Mar from 2007 to 2010. Patients were matched by age and sex with control subjects. A previously validated nutritional survey was administered to patients and controls. Demographic data, vascular risk factors, caloric intake and dietary nutrients were evaluated. Intention to follow a healthy diet was also assessed in both groups. Results A total of 300 acute ischemic stroke patients and 300 controls with evaluation of dietary habits. No differences were observed in vascular risk factors, except smoking habit, diabetes and ischemic heart disease. Stroke patients reported a higher caloric intake: 2444.8(1736.8–3244.5) vs 2208.7(1753.1–2860.7) Kcal, p = 0.001. After adjusting for energy intake, patients had higher intake of proteins (p<0.001; OR 1.02), total cholesterol (p = 0.001; OR 1.04), and breaded foods (p = 0.001; OR 1.94) and lower consumption of probiotic yogurt (p = 0.002; OR 0.88). Compared to patients, control participants indicated greater intention to eat vegetables (p = 0.002; OR 1.5) and whole foods (p = 0.000; OR 2.4) and reduce their intake of salt (p = 0.002; OR 1.7), fat (p = 0.000; OR 3.7) and sweets (p = 0.004; OR 1.7) than patients. Conclusion We observed different dietary patterns between stroke patients and controls. Stroke patients have a higher caloric intake and are less concerned about maintaining healthy nutritional habits.

Sex-related differences in abdominal obesity impact on ischemic stroke risk.

The objective of our study was to evaluate sex differences in the impact of weight and abdominal obesity on the risk of ischemic stroke.

New-Onset Paroxysmal Atrial Fibrillation Diagnosis in Ischemic Stroke Patients

Introduction: The aims of this study are to describe the incidence of paroxysmal atrial fibrillation (pAF) in patients with ischemic stroke (IS) or transient ischemic attack (TIA), and to create a risk prediction model, using immediately available clinical data associated with new pAF diagnosis. Methods: We analyzed data from the BASICMAR stroke register, with 5 inclusion criteria: (1) diagnosis of IS/TIA; (2) no history of AF or structural cardiopathy; (3) stroke unit (SU) monitoring after normal electrocardiogram in the emergency room; (4) complete etiologic study; and (5) 3-month follow-up. We investigated clinical predictors of pAF detection; we analyzed newly diagnosed pAF according to 4 cardiac monitoring screening methods and created a pAF-risk prediction model. Results: The final cohort included 1,240 patients. pAF was diagnosed in 139 patients (11.2%), the majority at the SU (54.7%). Multivariate predictors of new-pAF diagnosis during 3-month follow-up after ischemic event were age 75 years, female gender, history of congestive heart failure, and initial National Institute of Health Stroke Scale 15, with a predicted AF risk of 64%. Conclusions: This risk prediction model can be helpful to estimate the risk of an underlying pAF within 3 months after suffering an IS/TIA, contributing to increased AF detection efforts, thereby starting the correct secondary prevention treatment.