The Identification of a Threshold of Long Work Hours for Predicting Elevated Risks of Adverse Health Outcomes.

Working long hours has been associated with adverse health outcomes. However, a definition of long work hours relative to adverse health risk has not been established. Repeated measures of work hours among approximately 2,000 participants from the Panel Study of Income Dynamics (1986-2011), conducted in the United States, were retrospectively analyzed to derive statistically optimized cutpoints of long work hours that best predicted three health outcomes. Work-hours cutpoints were assessed for model fit, calibration, and discrimination separately for the outcomes of poor self-reported general health, incident cardiovascular disease, and incident cancer. For each outcome, the work-hours threshold that best predicted increased risk was 52 hours per week or more for a minimum of 10 years. Workers exposed at this level had a higher risk of poor self-reported general health (relative risk (RR) = 1.28; 95% confidence interval (CI): 1.06, 1.53), cardiovascular disease (RR = 1.42; 95% CI: 1.24, 1.63), and cancer (RR = 1.62; 95% CI: 1.22, 2.17) compared with those working 35-51 hours per week for the same duration. This study provides the first health risk-based definition of long work hours. Further examination of the predictive power of this cutpoint on other health outcomes and in other study populations is needed.

Gene × dietary pattern interactions in obesity: analysis of up to 68 317 adults of European ancestry.

Obesity is highly heritable. Genetic variants showing robust associations with obesity traits have been identified through genome-wide association studies. We investigated whether a composite score representing healthy diet modifies associations of these variants with obesity traits. Totally, 32 body mass index (BMI)- and 14 waist-hip ratio (WHR)-associated single nucleotide polymorphisms were genotyped, and genetic risk scores (GRS) were calculated in 18 cohorts of European ancestry (n = 68 317). Diet score was calculated based on self-reported intakes of whole grains, fish, fruits, vegetables, nuts/seeds (favorable) and red/processed meats, sweets, sugar-sweetened beverages and fried potatoes (unfavorable). Multivariable adjusted, linear regression within each cohort followed by inverse variance-weighted, fixed-effects meta-analysis was used to characterize: (a) associations of each GRS with BMI and BMI-adjusted WHR and (b) diet score modification of genetic associations with BMI and BMI-adjusted WHR. Nominally significant interactions (P = 0.006-0.04) were observed between the diet score and WHR-GRS (but not BMI-GRS), two WHR loci (GRB14 rs10195252; LYPLAL1 rs4846567) and two BMI loci (LRRN6C rs10968576; MTIF3 rs4771122), for the respective BMI-adjusted WHR or BMI outcomes. Although the magnitudes of these select interactions were small, our data indicated that associations between genetic predisposition and obesity traits were stronger with a healthier diet. Our findings generate interesting hypotheses; however, experimental and functional studies are needed to determine their clinical relevance.

Meta-analysis investigating associations between healthy diet and fasting glucose and insulin levels and modification by loci associated with glucose homeostasis in data from 15 cohorts.

Whether loci that influence fasting glucose (FG) and fasting insulin (FI) levels, as identified by genome-wide association studies, modify associations of diet with FG or FI is unknown. We utilized data from 15 U.S. and European cohort studies comprising 51,289 persons without diabetes to test whether genotype and diet interact to influence FG or FI concentration. We constructed a diet score using study-specific quartile rankings for intakes of whole grains, fish, fruits, vegetables, and nuts/seeds (favorable) and red/processed meats, sweets, sugared beverages, and fried potatoes (unfavorable). We used linear regression within studies, followed by inverse-variance-weighted meta-analysis, to quantify 1) associations of diet score with FG and FI levels and 2) interactions of diet score with 16 FG-associated loci and 2 FI-associated loci. Diet score (per unit increase) was inversely associated with FG (ß = -0.004 mmol/L, 95% confidence interval: -0.005, -0.003) and FI (ß = -0.008 ln-pmol/L, 95% confidence interval: -0.009, -0.007) levels after adjustment for demographic factors, lifestyle, and body mass index. Genotype variation at the studied loci did not modify these associations. Healthier diets were associated with lower FG and FI concentrations regardless of genotype at previously replicated FG- and FI-associated loci. Studies focusing on genomic regions that do not yield highly statistically significant associations from main-effect genome-wide association studies may be more fruitful in identifying diet-gene interactions.

Variability analysis of epileptic EEG using the maximal overlap discrete wavelet transform.

PURPOSE: To determine if there is a difference in the wavelet variances of seizure and non-seizure channels in the EEG of an epileptic subject. METHODS: A six-level decomposition was applied using the Maximal Overlap Discrete Wavelet Transform (MODWT). The wavelet variance and 95% CIs were calculated for each level of the decomposition. The number of changes in variance for each level were found using a change-point detection method of Whitcher. The Kruskal-Wallis test was used to determine if there were differences in the median number of change points within channels and across frequency bands (levels). RESULTS: No distinctive pattern was found for the wavelet variances to differentiate the seizure and non-seizure channels. The seizure channels tended to have lower variances for each level and overall, but this pattern only held for one of the three seizure channels (RAST4). The median number of change points did not differ between the seizure and non-seizure channels either within each channel or across the frequency bands. CONCLUSION: The use of the MODWT in examining the variances and changes in variance did not show specific patterns which differentiate between seizure and non-seizure channels.

Coffee intake, smoking, and pulmonary function in the Atherosclerosis Risk in Communities Study.

Coffee contains polyphenolic antioxidants and caffeine, which may favorably affect pulmonary function. Therefore, the authors studied cross-sectional associations (1987-1989) between coffee intake and pulmonary function in the Atherosclerosis Risk in Communities Study, a population-based cohort study (analytic sample = 10,658). They also conducted analyses stratified by smoking status, since smoking is a strong risk factor for respiratory disease and could influence the effects of caffeine and antioxidants. Self-reported coffee intake was categorized as rare/never, <7 cups/week, 1 cup/day, 2-3 cups/day, and >or=4 cups/day. Pulmonary function was characterized by the spirometric measures forced vital capacity (FVC) and forced expiratory volume in 1 second (FEV(1)). After adjustment for demographic factors, lifestyle characteristics, and dietary factors, pulmonary function values increased across increasing categories of coffee consumption in never and former smokers but not in current smokers. In never or former smokers who consumed >or=4 cups of coffee daily, FVC and FEV(1) were 2%-3% greater than in never or former smokers who rarely/never consumed coffee (P(trend) values: in never smokers, 0.04 for FVC and 0.07 for FEV(1); in former smokers, <0.001 for FVC and <0.001 for FEV(1)). These data show a possible beneficial effect of coffee (or a coffee ingredient) on pulmonary function, but it appears to be limited to nonsmokers.

Potential Interplay between Dietary Saturated Fats and Genetic Variants of the NLRP3 Inflammasome to Modulate Insulin Resistance and Diabetes Risk: Insights from a Meta-Analysis of 19 005 Individuals.

SCOPE: Insulin resistance (IR) and inflammation are hallmarks of type 2 diabetes (T2D). The nod-like receptor pyrin domain containing-3 (NLRP3) inflammasome is a metabolic sensor activated by saturated fatty acids (SFA) initiating IL-1ß inflammation and IR. Interactions between SFA intake and NLRP3-related genetic variants may alter T2D risk factors. METHODS: Meta-analyses of six Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (n = 19 005) tested interactions between SFA and NLRP3-related single-nucleotide polymorphisms (SNPs) and modulation of fasting insulin, fasting glucose, and homeostasis model assessment of insulin resistance. RESULTS: SFA interacted with rs12143966, wherein each 1% increase in SFA intake increased insulin by 0.0063 IU mL-1 (SE ± 0.002, p = 0.001) per each major (G) allele copy. rs4925663, interacted with SFA (ß ± SE = -0.0058 ± 0.002, p = 0.004) to increase insulin by 0.0058 IU mL-1 , per additional copy of the major (C) allele. Both associations are close to the significance threshold (p < 0.0001). rs4925663 causes a missense mutation affecting NLRP3 expression. CONCLUSION: Two NLRP3-related SNPs showed potential interaction with SFA to modulate fasting insulin. Greater dietary SFA intake accentuates T2D risk, which, subject to functional validation, may be further elaborated depending on NLRP3-related genetic variants.

Chronic obstructive pulmonary disease among lung cancer-free smokers: The importance of healthy controls.

BACKGROUND: The prevalence of chronic obstructive pulmonary disease (COPD) in smokers enrolled as "healthy" controls in studies is 10-50%. The COPD status of ideal smoker populations for lung cancer case-control studies should be checked via spirometry; however, this is often not feasible, because no medical indications exist for asymptomatic smokers to undergo spirometry prior to study enrollment. Therefore, there is an unmet need for robust, cost effective assays for identifying undiagnosed lung disease among asymptomatic smokers. Such assays would help excluding unhealthy smokers from lung cancer case-control studies. METHODS: We used the cytokinesis-blocked micronucleus (CBMN) assay (a measure of genetic instability) to identify undiagnosed lung disease among asymptomatic smokers. We used a convenience population from an on-going lung cancer case-control study including smokers with lung cancer (n = 454), smoker controls (n = 797), and a self-reported COPD (n = 200) contingent within the smoker controls. RESULTS: Significant differences for all CBMN endpoints were observed when comparing lung cancer to All controls (which included COPD) and Healthy controls (with no COPD). The risk ratio (RR) was increased in the COPD group vs. Healthy controls for nuclear buds (RR 1.28, 95% confidence interval 1.01-1.62), and marginally increased for micronuclei (RR 1.06, 0.98-1.89) and nucleoplasmic bridges (RR 1.07, 0.97-1.15). CONCLUSION: These findings highlight the importance of using truly healthy controls in studies geared toward assessment of lung cancer risk. Using genetic instability biomarkers would facilitate the identification of smokers susceptible to tobacco smoke carcinogens and therefore predisposed to either disease.

Genome-Wide Interactions with Dairy Intake for Body Mass Index in Adults of European Descent.

SCOPE: Body weight responds variably to the intake of dairy foods. Genetic variation may contribute to inter-individual variability in associations between body weight and dairy consumption. METHODS AND RESULTS: A genome-wide interaction study to discover genetic variants that account for variation in BMI in the context of low-fat, high-fat and total dairy intake in cross-sectional analysis was conducted. Data from nine discovery studies (up to 25 513 European descent individuals) were meta-analyzed. Twenty-six genetic variants reached the selected significance threshold (p-interaction <10-7) , and six independent variants (LINC01512-rs7751666, PALM2/AKAP2-rs914359, ACTA2-rs1388, PPP1R12A-rs7961195, LINC00333-rs9635058, AC098847.1-rs1791355) were evaluated meta-analytically for replication of interaction in up to 17 675 individuals. Variant rs9635058 (128 kb 3' of LINC00333) was replicated (p-interaction = 0.004). In the discovery cohorts, rs9635058 interacted with dairy (p-interaction = 7.36 × 10-8) such that each serving of low-fat dairy was associated with 0.225 kg m-2 lower BMI per each additional copy of the effect allele (A). A second genetic variant (ACTA2-rs1388) approached interaction replication significance for low-fat dairy exposure. CONCLUSION: Body weight responses to dairy intake may be modified by genotype, in that greater dairy intake may protect a genetic subgroup from higher body weight.

Dose-Response Relation Between Work Hours and Cardiovascular Disease Risk: Findings From the Panel Study of Income Dynamics.

OBJECTIVES: The aim of this study was to examine the presence of a dose-response relationship between work hours and incident cardiovascular disease (CVD) in a representative sample of U.S. workers. METHODS: A retrospective cohort study of 1926 individuals from the Panel Study of Income Dynamics (1986 to 2011) employed for at least 10 years. Restricted cubic spline regression was used to estimate the dose-response relationship of work hours with CVD. RESULTS: A dose-response relationship was observed in which an average workweek of 46 hours or more for at least 10 years was associated with an increased risk of CVD. Compared with working 45 hours per week, working an additional 10 hours per week or more for at least 10 years increased CVD risk by at least 16%. CONCLUSION: Working more than 45 work hours per week for at least 10 years may be an independent risk factor for CVD.

Interaction of methylation-related genetic variants with circulating fatty acids on plasma lipids: a meta-analysis of 7 studies and methylation analysis of 3 studies in the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium.

BACKGROUND: DNA methylation is influenced by diet and single nucleotide polymorphisms (SNPs), and methylation modulates gene expression. OBJECTIVE: We aimed to explore whether the gene-by-diet interactions on blood lipids act through DNA methylation. DESIGN: We selected 7 SNPs on the basis of predicted relations in fatty acids, methylation, and lipids. We conducted a meta-analysis and a methylation and mediation analysis with the use of data from the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) consortium and the ENCODE (Encyclopedia of DNA Elements) consortium. RESULTS: On the basis of the meta-analysis of 7 cohorts in the CHARGE consortium, higher plasma HDL cholesterol was associated with fewer C alleles at ATP-binding cassette subfamily A member 1 (ABCA1) rs2246293 (ß = -0.6 mg/dL, P = 0.015) and higher circulating eicosapentaenoic acid (EPA) (ß = 3.87 mg/dL, P = 5.62 × 10(21)). The difference in HDL cholesterol associated with higher circulating EPA was dependent on genotypes at rs2246293, and it was greater for each additional C allele (ß = 1.69 mg/dL, P = 0.006). In the GOLDN (Genetics of Lipid Lowering Drugs and Diet Network) study, higher ABCA1 promoter cg14019050 methylation was associated with more C alleles at rs2246293 (ß = 8.84%, P = 3.51 × 10(18)) and lower circulating EPA (ß = -1.46%, P = 0.009), and the mean difference in methylation of cg14019050 that was associated with higher EPA was smaller with each additional C allele of rs2246293 (ß = -2.83%, P = 0.007). Higher ABCA1 cg14019050 methylation was correlated with lower ABCA1 expression (r = -0.61, P = 0.009) in the ENCODE consortium and lower plasma HDL cholesterol in the GOLDN study (r = -0.12, P = 0.0002). An additional mediation analysis was meta-analyzed across the GOLDN study, Cardiovascular Health Study, and the Multi-Ethnic Study of Atherosclerosis. Compared with the model without the adjustment of cg14019050 methylation, the model with such adjustment provided smaller estimates of the mean plasma HDL cholesterol concentration in association with both the rs2246293 C allele and EPA and a smaller difference by rs2246293 genotypes in the EPA-associated HDL cholesterol. However, the differences between 2 nested models were NS (P > 0.05). CONCLUSION: We obtained little evidence that the gene-by-fatty acid interactions on blood lipids act through DNA methylation.

Gene-Environment Interactions of Circadian-Related Genes for Cardiometabolic Traits.

OBJECTIVE: Common circadian-related gene variants associate with increased risk for metabolic alterations including type 2 diabetes. However, little is known about whether diet and sleep could modify associations between circadian-related variants (CLOCK-rs1801260, CRY2-rs11605924, MTNR1B-rs1387153, MTNR1B-rs10830963, NR1D1-rs2314339) and cardiometabolic traits (fasting glucose [FG], HOMA-insulin resistance, BMI, waist circumference, and HDL-cholesterol) to facilitate personalized recommendations. RESEARCH DESIGN AND METHODS: We conducted inverse-variance weighted, fixed-effect meta-analyses of results of adjusted associations and interactions between dietary intake/sleep duration and selected variants on cardiometabolic traits from 15 cohort studies including up to 28,190 participants of European descent from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium. RESULTS: We observed significant associations between relative macronutrient intakes and glycemic traits and short sleep duration (<7 h) and higher FG and replicated known MTNR1B associations with glycemic traits. No interactions were evident after accounting for multiple comparisons. However, we observed nominally significant interactions (all P < 0.01) between carbohydrate intake and MTNR1B-rs1387153 for FG with a 0.003 mmol/L higher FG with each additional 1% carbohydrate intake in the presence of the T allele, between sleep duration and CRY2-rs11605924 for HDL-cholesterol with a 0.010 mmol/L higher HDL-cholesterol with each additional hour of sleep in the presence of the A allele, and between long sleep duration (≥9 h) and MTNR1B-rs1387153 for BMI with a 0.60 kg/m(2) higher BMI with long sleep duration in the presence of the T allele relative to normal sleep duration (≥7 to <9 h). CONCLUSIONS: Our results suggest that lower carbohydrate intake and normal sleep duration may ameliorate cardiometabolic abnormalities conferred by common circadian-related genetic variants. Until further mechanistic examination of the nominally significant interactions is conducted, recommendations applicable to the general population regarding diet­specifically higher carbohydrate and lower fat composition­and normal sleep duration should continue to be emphasized among individuals with the investigated circadian-related gene variants.

Dietary fatty acids modulate associations between genetic variants and circulating fatty acids in plasma and erythrocyte membranes: Meta-analysis of nine studies in the CHARGE consortium.

SCOPE: Tissue concentrations of omega-3 fatty acids may reduce cardiovascular disease risk, and genetic variants are associated with circulating fatty acids concentrations. Whether dietary fatty acids interact with genetic variants to modify circulating omega-3 fatty acids is unclear. We evaluated interactions between genetic variants and fatty acid intakes for circulating alpha-linoleic acid, eicosapentaenoic acid, docosahexaenoic acid, and docosapentaenoic acid. METHODS AND RESULTS: We conducted meta-analyses (N = 11 668) evaluating interactions between dietary fatty acids and genetic variants (rs174538 and rs174548 in FADS1 (fatty acid desaturase 1), rs7435 in AGPAT3 (1-acyl-sn-glycerol-3-phosphate), rs4985167 in PDXDC1 (pyridoxal-dependent decarboxylase domain-containing 1), rs780094 in GCKR (glucokinase regulatory protein), and rs3734398 in ELOVL2 (fatty acid elongase 2)). Stratification by measurement compartment (plasma versus erthyrocyte) revealed compartment-specific interactions between FADS1 rs174538 and rs174548 and dietary alpha-linolenic acid and linoleic acid for docosahexaenoic acid and docosapentaenoic acid. CONCLUSION: Our findings reinforce earlier reports that genetically based differences in circulating fatty acids may be partially due to differences in the conversion of fatty acid precursors. Further, fatty acids measurement compartment may modify gene-diet relationships, and considering compartment may improve the detection of gene-fatty acids interactions for circulating fatty acid outcomes.

Consumption of meat is associated with higher fasting glucose and insulin concentrations regardless of glucose and insulin genetic risk scores: a meta-analysis of 50,345 Caucasians.

BACKGROUND: Recent studies suggest that meat intake is associated with diabetes-related phenotypes. However, whether the associations of meat intake and glucose and insulin homeostasis are modified by genes related to glucose and insulin is unknown. OBJECTIVE: We investigated the associations of meat intake and the interaction of meat with genotype on fasting glucose and insulin concentrations in Caucasians free of diabetes mellitus. DESIGN: Fourteen studies that are part of the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium participated in the analysis. Data were provided for up to 50,345 participants. Using linear regression within studies and a fixed-effects meta-analysis across studies, we examined 1) the associations of processed meat and unprocessed red meat intake with fasting glucose and insulin concentrations; and 2) the interactions of processed meat and unprocessed red meat with genetic risk score related to fasting glucose or insulin resistance on fasting glucose and insulin concentrations. RESULTS: Processed meat was associated with higher fasting glucose, and unprocessed red meat was associated with both higher fasting glucose and fasting insulin concentrations after adjustment for potential confounders [not including body mass index (BMI)]. For every additional 50-g serving of processed meat per day, fasting glucose was 0.021 mmol/L (95% CI: 0.011, 0.030 mmol/L) higher. Every additional 100-g serving of unprocessed red meat per day was associated with a 0.037-mmol/L (95% CI: 0.023, 0.051-mmol/L) higher fasting glucose concentration and a 0.049-ln-pmol/L (95% CI: 0.035, 0.063-ln-pmol/L) higher fasting insulin concentration. After additional adjustment for BMI, observed associations were attenuated and no longer statistically significant. The association of processed meat and fasting insulin did not reach statistical significance after correction for multiple comparisons. Observed associations were not modified by genetic loci known to influence fasting glucose or insulin resistance. CONCLUSION: The association of higher fasting glucose and insulin concentrations with meat consumption was not modified by an index of glucose- and insulin-related single-nucleotide polymorphisms. Six of the participating studies are registered at clinicaltrials.gov as NCT0000513 (Atherosclerosis Risk in Communities), NCT00149435 (Cardiovascular Health Study), NCT00005136 (Family Heart Study), NCT00005121 (Framingham Heart Study), NCT00083369 (Genetics of Lipid Lowering Drugs and Diet Network), and NCT00005487 (Multi-Ethnic Study of Atherosclerosis).

Habitual sleep duration is associated with BMI and macronutrient intake and may be modified by CLOCK genetic variants.

BACKGROUND: Short sleep duration has been associated with greater risks of obesity, hypertension, diabetes, and cardiovascular disease. Also, common genetic variants in the human Circadian Locomotor Output Cycles Kaput (CLOCK) show associations with ghrelin and total energy intake. OBJECTIVES: We examined associations between habitual sleep duration, body mass index (BMI), and macronutrient intake and assessed whether CLOCK variants modify these associations. DESIGN: We conducted inverse-variance weighted, fixed-effect meta-analyses of results of adjusted associations of sleep duration and BMI and macronutrient intake as percentages of total energy as well as interactions with CLOCK variants from 9 cohort studies including up to 14,906 participants of European descent from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium. RESULTS: We observed a significant association between sleep duration and lower BMI (ß ± SE = 0.16 ± 0.04, P < 0.0001) in the overall sample; however, associations between sleep duration and relative macronutrient intake were evident in age- and sex-stratified analyses only. We observed a significant association between sleep duration and lower saturated fatty acid intake in younger (aged 20-64 y) adults (men: 0.11 ± 0.06%, P = 0.03; women: 0.10 ± 0.05%, P = 0.04) and with lower carbohydrate (-0.31 ± 0.12%, P < 0.01), higher total fat (0.18 ± 0.09%, P = 0.05), and higher PUFA (0.05 ± 0.02%, P = 0.02) intakes in older (aged 65-80 y) women. In addition, the following 2 nominally significant interactions were observed: between sleep duration and rs12649507 on PUFA intake and between sleep duration and rs6858749 on protein intake. CONCLUSIONS: Our results indicate that longer habitual sleep duration is associated with lower BMI and age- and sex-specific favorable dietary behaviors. Differences in the relative intake of specific macronutrients associated with short sleep duration could, at least in part, explain previously reported associations between short sleep duration and chronic metabolic abnormalities. In addition, the influence of obesity-associated CLOCK variants on the association between sleep duration and macronutrient intake suggests that longer habitual sleep duration could ameliorate the genetic predisposition to obesity via a favorable dietary profile.

Using a PyMOL activity to reinforce the connection between genotype and phenotype in an undergraduate genetics laboratory.

With the purpose of developing an activity that would help clarify genetic concepts related to the connection between genotype and phenotype and the nature of mutations, we designed a three hour teaching module using the PyMol software. The activity starts with two pre-laboratory assignments, one to learn how to use PyMol and the other to read about a specific protein or protein family. During the laboratory students are given instructions where and how to find additional information on a specific disease and its causal mutations in order to prepare a 10-minute, in-class presentation. Using a post activity, anonymous quiz, we found a statistically significant different grade distribution in students that participated in the PyMol activity relative to a control group. We also found a significant improvement in the student's comprehension when answering questions regarding the nature of mutations and protein structure. This demonstrates the utility of this simulation activity as a vehicle to improve student's understanding of specific key genetic concepts.

Associations between dietary patterns and flow cytometry-measured biomarkers of inflammation and cellular activation in the Atherosclerosis Risk in Communities (ARIC) Carotid Artery MRI Study.

BACKGROUND: Specific foods and overall dietary patterns are associated with soluble biomarkers of systemic inflammation and endothelial activation. However, no large epidemiological studies have evaluated relationships between such dietary factors and cell-specific markers of activation and inflammation as measured by flow cytometry. METHODS: Cell aggregates and multiple platelet and leukocyte markers were quantified by flow cytometry in fresh whole blood from 1101 white adults participating in the Carotid Artery MRI Study, a subset of the larger Atherosclerosis Risk in Communities (ARIC) Study. Two dietary patterns ("Healthy" and "Western") were empirically derived via principal components analysis using data collected by food frequency questionnaire. Cross-sectional associations between dietary patterns and flow cytometry-measured biomarkers were evaluated, adjusting for demographics and lifestyle factors, including medications use. RESULTS: After multivariable adjustment, monocyte lipopolysaccharide receptor (CD14), monocyte toll-like receptor-2, and platelet glycoprotein IIb (CD41) showed inverse associations with the Healthy dietary pattern (p=0.01, 0.04, and 0.01, respectively). In contrast, the Western dietary pattern was positively associated with CD41 and platelet-granulocyte aggregates (p=0.01 and 0.04, respectively). Independent of other dietary factors, alcohol consumption was inversely associated with levels of pan-leukocyte marker (CD45), P-selectin (CD62P) on PLA1 and on PLA2 platelets, and platelet-monocyte, platelet-granulocyte, and platelet-lymphocyte aggregates. CONCLUSION: Dietary patterns and alcohol intake were each cross-sectionally associated with select markers of cellular activation and inflammation measured by flow cytometry. These data are consistent with the hypothesis that holistic measures of dietary intake are associated with inflammation.

Interactions of dietary whole-grain intake with fasting glucose- and insulin-related genetic loci in individuals of European descent: a meta-analysis of 14 cohort studies.

OBJECTIVE: Whole-grain foods are touted for multiple health benefits, including enhancing insulin sensitivity and reducing type 2 diabetes risk. Recent genome-wide association studies (GWAS) have identified several single nucleotide polymorphisms (SNPs) associated with fasting glucose and insulin concentrations in individuals free of diabetes. We tested the hypothesis that whole-grain food intake and genetic variation interact to influence concentrations of fasting glucose and insulin. RESEARCH DESIGN AND METHODS: Via meta-analysis of data from 14 cohorts comprising ∼ 48,000 participants of European descent, we studied interactions of whole-grain intake with loci previously associated in GWAS with fasting glucose (16 loci) and/or insulin (2 loci) concentrations. For tests of interaction, we considered a P value <0.0028 (0.05 of 18 tests) as statistically significant. RESULTS: Greater whole-grain food intake was associated with lower fasting glucose and insulin concentrations independent of demographics, other dietary and lifestyle factors, and BMI (ß [95% CI] per 1-serving-greater whole-grain intake: -0.009 mmol/l glucose [-0.013 to -0.005], P < 0.0001 and -0.011 pmol/l [ln] insulin [-0.015 to -0.007], P = 0.0003). No interactions met our multiple testing-adjusted statistical significance threshold. The strongest SNP interaction with whole-grain intake was rs780094 (GCKR) for fasting insulin (P = 0.006), where greater whole-grain intake was associated with a smaller reduction in fasting insulin concentrations in those with the insulin-raising allele. CONCLUSIONS: Our results support the favorable association of whole-grain intake with fasting glucose and insulin and suggest a potential interaction between variation in GCKR and whole-grain intake in influencing fasting insulin concentrations.