Background Dietary intake of n-3 polyunsaturated fatty acids (PUFA) may have a protective effect on the development of cardiovascular diseases, diabetes, depression and cancer, while a high intake... Show moreBackground Dietary intake of n-3 polyunsaturated fatty acids (PUFA) may have a protective effect on the development of cardiovascular diseases, diabetes, depression and cancer, while a high intake of n-6 PUFA was often reported to be associated with inflammation-related traits. The effect of PUFAs on health outcomes might be mediated by DNA methylation (DNAm). The aim of our study is to identify the impact of PUFA intake on DNAm in the Cooperative Health Research in the Region of Augsburg (KORA) FF4 cohort and the Leiden Longevity Study (LLS). Results DNA methylation levels were measured in whole blood from the population-based KORA FF4 study (N = 1354) and LLS (N = 448), using the Illumina MethylationEPIC BeadChip and Illumina HumanMethylation450 array, respectively. We assessed associations between DNAm and intake of eight and four PUFAs in KORA and LLS, respectively. Where possible, results were meta-analyzed.Below the Bonferroni correction threshold (p < 7.17 x 10(-8)), we identified two differentially methylated positions (DMPs) associated with PUFA intake in the KORA study. The DMP cg19937480, annotated to gene PRDX1, was positively associated with docosahexaenoic acid (DHA) in model 1 (beta: 2.00 x 10(-5), 95%CI: 1.28 x 10(-5)-2.73 x 10(-5), P value: 6.98 x 10(-8)), while cg05041783, annotated to gene MARK2, was positively associated with docosapentaenoic acid (DPA) in our fully adjusted model (beta: 9.80 x 10(-5), 95%CI: 6.25 x 10(-5)-1.33 x 10(-4), P value: 6.75 x 10(-8)). In the meta-analysis, we identified the CpG site (cg15951061), annotated to gene CDCA7L below Bonferroni correction (1.23 x 10(-7)) associated with eicosapentaenoic acid (EPA) intake in model 1 (beta: 2.00 x 10(-5), 95% CI: 1.27 x 10(-5)-2.73 x 10(-5), P value = 5.99 x 10(-8)) and we confirmed the association of cg19937480 with DHA in both models 1 and 2 (beta: 2.07 x 10(-5), 95% CI: 1.31 x 10(-5)-2.83 x 10(-5), P value = 1.00 x 10(-7) and beta: 2.19 x 10(-5), 95% CI: 1.41 x 10(-5)-2.97 x 10(-5), P value = 5.91 x 10(-8) respectively).Conclusions Our study identified three CpG sites associated with PUFA intake. The mechanisms of these sites remain largely unexplored, highlighting the novelty of our findings. Further research is essential to understand the links between CpG site methylation and PUFA outcomes. Show less
Narcolepsy type 1 (NT1) is caused by a loss of hypocretin/orexin transmission. Risk factors include pandemic 2009 H1N1 influenza A infection and immunization with Pandemrix®. Here, we dissect... Show moreNarcolepsy type 1 (NT1) is caused by a loss of hypocretin/orexin transmission. Risk factors include pandemic 2009 H1N1 influenza A infection and immunization with Pandemrix®. Here, we dissect disease mechanisms and interactions with environmental triggers in a multi-ethnic sample of 6,073 cases and 84,856 controls. We fine-mapped GWAS signals within HLA (DQ0602, DQB1*03:01 and DPB1*04:02) and discovered seven novel associations (CD207, NAB1, IKZF4-ERBB3, CTSC, DENND1B, SIRPG, PRF1). Significant signals at TRA and DQB1*06:02 loci were found in 245 vaccination-related cases, who also shared polygenic risk. T cell receptor associations in NT1 modulated TRAJ*24, TRAJ*28 and TRBV*4-2 chain-usage. Partitioned heritability and immune cell enrichment analyses found genetic signals to be driven by dendritic and helper T cells. Lastly comorbidity analysis using data from FinnGen, suggests shared effects between NT1 and other autoimmune diseases. NT1 genetic variants shape autoimmunity and response to environmental triggers, including influenza A infection and immunization with Pandemrix®. Show less
Hellbach, F.; Sinke, L.; Costeira, R.; Baumeister, S.E.; Beekman, M.; Louca, P.; ... ; Linseisen, J. 2022
Purpose Examining epigenetic patterns is a crucial step in identifying molecular changes of disease pathophysiology, with DNA methylation as the most accessible epigenetic measure. Diet is... Show morePurpose Examining epigenetic patterns is a crucial step in identifying molecular changes of disease pathophysiology, with DNA methylation as the most accessible epigenetic measure. Diet is suggested to affect metabolism and health via epigenetic modifications. Thus, our aim was to explore the association between food consumption and DNA methylation. Methods Epigenome-wide association studies were conducted in three cohorts: KORA FF4, TwinsUK, and Leiden Longevity Study, and 37 dietary exposures were evaluated. Food group definition was harmonized across the three cohorts. DNA methylation was measured using Infinium MethylationEPIC BeadChip in KORA and Infinium HumanMethylation450 BeadChip in the Leiden study and the TwinsUK study. Overall, data from 2293 middle-aged men and women were included. A fixed-effects meta-analysis pooled study-specific estimates. The significance threshold was set at 0.05 for false-discovery rate-adjusted p values per food group. Results We identified significant associations between the methylation level of CpG sites and the consumption of onions and garlic (2), nuts and seeds (18), milk (1), cream (11), plant oils (4), butter (13), and alcoholic beverages (27). The signals targeted genes of metabolic health relevance, for example, GLI1, RPTOR, and DIO1, among others. Conclusion This EWAS is unique with its focus on food groups that are part of a Western diet. Significant findings were mostly related to food groups with a high-fat content. Show less
Studying individuals with extreme phenotypes could facilitate the understanding of disease modification by genetic or environmental factors. Our aim was to identify Huntington's disease (HD)... Show moreStudying individuals with extreme phenotypes could facilitate the understanding of disease modification by genetic or environmental factors. Our aim was to identify Huntington's disease (HD) patients with extreme symbol digit modality test (SDMT) scores. We first examined in HD the contribution of cognitive measures of the Unified Huntington's Disease Rating Scale (UHDRS) in predicting clinical endpoints. The language-independent SDMT was used to identify patients performing very well or very poorly relative to their CAG and age cohort. We used data from REGISTRY and COHORT observational study participants (5,603 HD participants with CAG repeats above 39 with 13,868 visits) and of 1,006 healthy volunteers (with 2,241 visits), included to identify natural aging and education effects on cognitive measures. Separate Cox proportional hazards models with CAG, age at study entry, education, sex, UHDRS total motor score and cognitive (SDMT, verbal fluency, Stroop tests) scores as covariates were used to predict clinical endpoints. Quantile regression for longitudinal language-independent SDMT data was used for boundary (2.5% and 97.5% quantiles) estimation and extreme score analyses stratified by age, education, and CAG repeat length. Ten percent of HD participants had an extreme SDMT phenotype for at least one visit. In contrast, only about 3% of participants were consistent SDMT extremes at two or more visits. The thresholds for the one-visit and two-visit extremes can be used to classify existing and new individuals. The identification of these phenotype extremes can be useful in the search for disease modifiers. Show less
