Genetic variants in lipid metabolism are independently associated with multiple features of the metabolic syndrome

<p>Abstract</p> <p>Background</p> <p>Our objective was to find single nucleotide polymorphisms (SNPs), within transcriptional pathways of glucose and lipid metabolism, which are related to multiple features of the metabolic syndrome (MetS).</p> <p>Methods</p> <p>373 SNPs were measured in 3575 subjects of the Doetinchem cohort. Prevalence of MetS features, i.e. hyperglycemia, abdominal obesity, decreased HDL-cholesterol levels and hypertension, were measured twice in 6 years. Associations between the SNPs and the individual MetS features were analyzed by log-linear models. For SNPs related to multiple MetS features (P < 0.01), we investigated whether these associations were independent of each other.</p> <p>Results</p> <p>Two SNPs, <it>CETP Ile405Val </it>and <it>APOE Cys112Arg</it>, were associated with both the prevalence of low HDL-cholesterol level (<it>Ile405Val </it>P = < .0001; <it>Cys112Arg </it>P = 0.001) and with the prevalence of abdominal obesity (<it>Ile405Val </it>P = 0.007; <it>Cys112Arg </it>P = 0.007). For both SNPs, the association with HDL-cholesterol was partly independent of the association with abdominal obesity and vice versa.</p> <p>Conclusion</p> <p>Two SNPs, mainly known for their role in lipid metabolism, were associated with two MetS features i.e., low HDL-cholesterol concentration, as well as, independent of this association, abdominal obesity. These SNPs may help to explain why low HDL-cholesterol levels and abdominal obesity frequently co-occur.</p

Diet, Physical Activity, and Daylight Exposure Patterns in Night-Shift Workers and Day Workers

Background: Night-shift work has been reported to have an impact on nutrition, daylight exposure, and physical activity, which might play a role in observed health effects. Because these exposures show diurnal variation, and shift work has been related with disturbances in the circadian rhythm, the timing of assessment of these factors requires careful consideration. Our aim was to describe the changes in patterns of diet, physical activity, and daylight exposure associated with night-shift work. Methods: We conducted an observational study among female healthcare workers either regularly working night shifts or not working night shifts. We assessed physical activity and daylight exposure using continuous monitoring devices for 48 h. We logged dietary patterns (24 h) and other health- and work-associated characteristics. Two measurement sessions were conducted when participants did 'not' work night shifts, and one session was conducted during a night-shift period. Results: Our study included 69 night-shift workers and 21 day workers. On days in which they conduct work but no night work, night-shift workers had similar physical activity and 24-h caloric intake, yet higher overall daylight exposures than day workers and were more often exposed around noon instead of mainly around 1800h. Night-shift workers were less exposed to daylight during the night-shift session compared to the non-night-shift session. Total caloric intakes did not significantly differ between sessions, but we did observe a shorter maximum fasting interval, more eating moments, and a higher percentage of fat intake during the night-shift session. Conclusion: Observed differences in diet, physical activity, and exposure to daylight primarily manifested themselves through changes in exposure patterns, highlighting the importance of time-resolved measurements in night-shift-work research. Patterns in daylight exposure were primarily related to time of waking up and working schedule, whereas timing of dinner seemed primarily governed by social conventions

Tissue-Specific Suppression of Thyroid Hormone Signaling in Various Mouse Models of Aging

DNA damage contributes to the process of aging, as underscored by premature aging syndromes caused by defective DNA repair. Thyroid state changes during aging, but underlying mechanisms remain elusive. Since thyroid hormone (TH) is a key regulator of metabolism, changes in TH signaling have widespread effects. Here, we reveal a significant common transcriptomic signature in livers from hypothyroid mice, DNA repair-deficient mice with severe (Csbm/m/Xpa-/-) or intermediate (Ercc1-/Δ-7) progeria and naturally aged mice. A strong induction of TH-inactivating deiodinase D3 and decrease of TH-activating D1 activities are observed in Csbm/m/Xpa-/- livers. Similar findings are noticed in Ercc1-/Δ-7, in naturally aged animals and in wild-type mice exposed to a chronic subtoxic dose of DNA-damaging agents. In contrast, TH signaling in muscle, heart and brain appears unaltered. These data show a strong suppression of TH signaling in specific peripheral organs in premature and normal aging, probably lowering metabolism, while other tissues appear to preserve metabolism. D3-mediated TH inactivation is unexpected, given its expression mainly in fetal tissues. Our studies highlight the importance of DNA damage as the underlying mechanism of changes in thyroid state. Tissue-specific regulation of deiodinase activities, ensuring diminished TH signaling, may contribute importantly to the protective metabolic response in aging.status: publishe

Dietary n-3 and n-6 polyunsaturated fatty acid intake interacts with FADS1 genetic variation to affect total and HDL-cholesterol concentrations in the Doetinchem Cohort Study

The delta-5 and delta-6 desaturases, encoded by the FADS1 and FADS2 genes, are rate-limiting enzymes in polyunsaturated fatty acid (PUFA) biosynthesis. Single nucleotide polymorphisms in the FADS gene cluster region have been associated with both PUFA concentrations in plasma or erythrocyte membrane phospholipids and cholesterol concentrations in recent genome-wide association studies

Additional file 3: of Identifying and correcting epigenetics measurements for systematic sources of variation

Figure S3. Quantile-quantile (QQ) plots for CpG site-specific analysis with respect to smoking using standard adjustment (a), residuals (b), ComBat (c) and SVA (d) correcting methods for the M values. The inflation factor λ is defined as the ratio of the median of the observed log10 transformed p values from the CpG site-specific analysis and the median of the expected log10 transformed p values. (PDF 110 kb

Additional file 2: of Identifying and correcting epigenetics measurements for systematic sources of variation

Figure S2. Quantile-quantile (QQ) plots for CpG site-specific analysis with respect to smoking using standard adjustment (a), residuals (b), ComBat (c) and SVA (d) correcting methods for the β values. The inflation factor λ is defined as the ratio of the median of the observed log10 transformed p values from the CpG site-specific analysis and the median of the expected log10 transformed p values. (PDF 110 kb

Identifying and correcting epigenetics measurements for systematic sources of variation

Abstract Background Methylation measures quantified by microarray techniques can be affected by systematic variation due to the technical processing of samples, which may compromise the accuracy of the measurement process and contribute to bias the estimate of the association under investigation. The quantification of the contribution of the systematic source of variation is challenging in datasets characterized by hundreds of thousands of features. In this study, we introduce a method previously developed for the analysis of metabolomics data to evaluate the performance of existing normalizing techniques to correct for unwanted variation. Illumina Infinium HumanMethylation450K was used to acquire methylation levels in over 421,000 CpG sites for 902 study participants of a case-control study on breast cancer nested within the EPIC cohort. The principal component partial R-square (PC-PR2) analysis was used to identify and quantify the variability attributable to potential systematic sources of variation. Three correcting techniques, namely ComBat, surrogate variables analysis (SVA) and a linear regression model to compute residuals were applied. The impact of each correcting method on the association between smoking status and DNA methylation levels was evaluated, and results were compared with findings from a large meta-analysis. Results A sizeable proportion of systematic variability due to variables expressing ‘batch’ and ‘sample position’ within ‘chip’ was identified, with values of the partial R2 statistics equal to 9.5 and 11.4% of total variation, respectively. After application of ComBat or the residuals’ methods, the contribution was 1.3 and 0.2%, respectively. The SVA technique resulted in a reduced variability due to ‘batch’ (1.3%) and ‘sample position’ (0.6%), and in a diminished variability attributable to ‘chip’ within a batch (0.9%). After ComBat or the residuals’ corrections, a larger number of significant sites (k = 600 and k = 427, respectively) were associated to smoking status than the SVA correction (k = 96). Conclusions The three correction methods removed systematic variation in DNA methylation data, as assessed by the PC-PR2, which lent itself as a useful tool to explore variability in large dimension data. SVA produced more conservative findings than ComBat in the association between smoking and DNA methylation