Multi-Ancestry Genome-wide Association Study Accounting for Gene-Psychosocial Factor Interactions Identifies Novel Loci for Blood Pressure Traits.

Psychological and social factors are known to influence blood pressure (BP) and risk of hypertension and associated cardiovascular diseases. To identify novel BP loci, we carried out genome-wide association meta-analyses of systolic, diastolic, pulse, and mean arterial BP taking into account the interaction effects of genetic variants with three psychosocial factors: depressive symptoms, anxiety symptoms, and social support. Analyses were performed using a two-stage design in a sample of up to 128,894 adults from 5 ancestry groups. In the combined meta-analyses of Stages 1 and 2, we identified 59 loci (p value <5e-8), including nine novel BP loci. The novel associations were observed mostly with pulse pressure, with fewer observed with mean arterial pressure. Five novel loci were identified in African ancestry, and all but one showed patterns of interaction with at least one psychosocial factor. Functional annotation of the novel loci supports a major role for genes implicated in the immune response (PLCL2), synaptic function and neurotransmission (LIN7A, PFIA2), as well as genes previously implicated in neuropsychiatric or stress-related disorders (FSTL5, CHODL). These findings underscore the importance of considering psychological and social factors in gene discovery for BP, especially in non-European populations.

Assessment of reproducibility and biological variability of fasting and postprandial plasma metabolite concentrations using 1H NMR spectroscopy.

INTRODUCTION: It is crucial to understand the factors that introduce variability before applying metabolomics to clinical and biomarker research. OBJECTIVES: We quantified technical and biological variability of both fasting and postprandial metabolite concentrations measured using 1H NMR spectroscopy in plasma samples. METHODS: In the Netherlands Epidemiology of Obesity study (n = 6,671), 148 metabolite concentrations (101 metabolites belonging to lipoprotein subclasses) were measured under fasting and postprandial states (150 minutes after a mixed liquid meal). Technical variability was evaluated among 265 fasting and 851 postprandial samples, with the identical blood plasma sample being measured twice by the same laboratory protocol. Biological reproducibility was assessed by measuring 165 individuals twice across time for evaluation of short- (<6 months) and long-term (>3 years) biological variability. Intra-class coefficients (ICCs) were used to assess variability. The ICCs of the fasting metabolites were compared with the postprandial metabolites using two-sided paired Wilcoxon test separately for short- and long-term measurements. RESULTS: Both fasting and postprandial metabolite concentrations showed high technical reproducibility using 1H NMR spectroscopy (median ICC = 0.99). Postprandial metabolite concentrations revealed slightly higher ICC scores than fasting ones in short-term repeat measures (median ICC in postprandial and fasting metabolite concentrations 0.72 versus 0.67, Wilcoxon p-value = 8.0×10-14). Variability did not increase further in a long-term repeat measure, with median ICC in postprandial of 0.64 and in fasting metabolite concentrations 0.66. CONCLUSION: Technical reproducibility is excellent. Biological reproducibility of postprandial metabolite concentrations showed a less or equal variability than fasting metabolite concentrations over time.

The Association between Habitual Sleep Duration and Sleep Quality with Glycemic Traits: Assessment by Cross-Sectional and Mendelian Randomization Analyses.

Evidence on whether habitual sleep duration and sleep quality are associated with increased insulin resistance is inconsistent. Here, we investigated the associations between different measures of habitual sleep with glycemic traits through cross-sectional and Mendelian randomization (MR) analyses. We assessed the associations of sleep duration and sleep quality with glycemic traits using multivariable linear regression models adjusted for potential confounders in 4672 middle-aged (45-65 years; 48% men) nondiabetic participants of the Netherlands Epidemiology of Obesity (NEO) study. Genetic variants for total, short, and long sleep duration were used as instrumental variables in MR analyses using summary-level data of glycemic traits in nondiabetic individuals (MAGIC; n = 58,074). In cross-sectional analyses, shortest sleepers (median 5.0 h of sleep per night) had 14.5% (95% confidence interval (CI): 2.0; 28.6%) higher fasting insulin level and 16.3% (95% CI: 2.7; 31.7%) higher HOMA-ß. Bad sleep quality was associated with higher insulin resistance (e.g., 14.3% (95% CI: 4.7; 24.9%) higher HOMA-IR). All these associations disappeared after adjustment for BMI and the risk of sleep apnea. MR analyses did not indicate a causal association between total, short or long sleep duration and glycemic traits. Therefore, our used measures of habitual sleep duration and sleep quality are unlikely to directly associate with insulin resistance.

Overweight can be used as a tool to guide case-finding for cardiovascular risk assessment.

BACKGROUND: In general practice, it is too time-consuming to invite all patients for cardiovascular risk assessment. OBJECTIVE: To examine how many patients with an indication for treatment with cardiovascular medication can be identified by ad hoc case-finding when all patients with overweight/obesity are invited for risk assessment. METHODS: A cross-sectional analysis of the baseline measurements of the Netherlands Epidemiology of Obesity study, a population-based prospective cohort study in 6673 persons aged 45-65 years. We calculated the proportion of participants with a treatment indication using the risk prediction Systematic COronary Risk Evaluation (SCORE-NL 2011), for lean, overweight and obese participants. Participants with a history of cardiovascular disease, diabetes mellitus or rheumatoid arthritis or using cardiovascular medication were not eligible for ad hoc case-finding because they were already identified as being at risk and/or had been treated. RESULTS: Of the study population, 30% had already been identified and/or treated with cardiovascular medication and were therefore not eligible for ad hoc case-finding. Of the eligible participants, 47% were lean, 41% overweight and 12% obese. Of the participants with overweight, 12% had a treatment indication and of the participants with obesity, 19% had a treatment indication. Of all participants with a treatment indication 24% were not yet treated. Of all participants with a new treatment indication, 70% had overweight or obesity. CONCLUSIONS: Of the participants with a treatment indication, 24% were not yet treated. Inviting patients with overweight/obesity for cardiovascular risk assessment may help to detect 70% of these residual patients with a treatment indication.

Smoking is associated with increased resting energy expenditure in the general population: The NEO study.

OBJECTIVE: Animal studies and human studies in small selected populations have shown a positive association between nicotine smoking and resting energy expenditure (REE), but data in large cohorts are lacking. We aimed to investigate the association between smoking behavior and REE in a large, population-based study. DESIGN: Population-based cross-sectional study. METHODS: In this cross-sectional analysis of baseline measurements from the Netherlands Epidemiology of Obesity (NEO) study (n=6673), we included participants with REE measurement by indirect calorimetry who were not using lipid or glucose lowering drugs (n=1189). We used linear regression analysis to examine the association of smoking status (never, former, occasional, current smoker) and smoking quantity (pack years) with REE per kilogram (kg) fat free mass (FFM) and with REE adjusted for FFM. Models were adjusted for age, sex, ethnicity, educational level, physical activity, energy intake and body mass index (BMI). RESULTS: Mean (standard deviation, SD) age was 55.2 (5.9) years and BMI was 26.3 (4.4) kg/m(2). 60% of the participants were women. Mean (SD) REE/FFM (kcal/day/kg FFM) was for male never smokers 25.1 (2.0), male current smokers 26.4 (2.8), female never smokers 28.9 (2.5) and female current smokers 30.1 (3.7). After adjustment, only current smokers had a higher REE/FFM (mean difference 1.28, 95% CI 0.64, 1.92), and a higher REE adjusted for FFM (mean difference 60.3 kcal/day, 95% CI 29.1, 91.5), compared with never smokers. There was no association between pack years and REE/FFM (mean difference -0.01, 95% CI -0.06, 0.04) or REE adjusted for FFM (mean difference 0.2, 95% CI -2.4, 2.8) in current smokers. CONCLUSION: Current smoking is associated with a higher resting energy expenditure compared with never smoking in a large population-based cohort.

Subjective global assessment of nutritional status is strongly associated with mortality in chronic dialysis patients.

BACKGROUND: The subjective global assessment of nutritional status (SGA) is used to assess the nutritional status of chronic dialysis patients, but longitudinal data in relation to mortality risk are lacking. OBJECTIVE: Our objective was to study the long-term and time-dependent associations of the SGA with mortality risk in chronic dialysis patients. DESIGN: In a prospective, longitudinal, observational, multicenter study of incident dialysis patients, the 7-point SGA [7 = normal nutritional status; 1 = severe protein-energy wasting (PEW)] was assessed 3 and 6 mo after the start of dialysis and subsequently every 6 mo during 7 y of follow-up. With Cox regression analysis, we calculated hazard ratios (HRs) of the baseline and time-dependent SGA measurements, adjusted for age, sex, treatment modality, primary kidney diseases, and comorbidity. RESULTS: In total, 1601 patients were included [mean (+/-SD) age: 59 +/- 15 y; 61% men; 23% with moderate PEW (SGA(4-5)), and 5% with severe PEW (SGA(1-3))]. There was a dose-dependent trend of the 7-point SGA with mortality. Compared with a normal nutritional status at baseline, SGA(4-5) (HR: 1.6; 95% CI: 1.3, 1.9) and SGA(1-3) (HR: 2.1; 95% CI: 1.5, 2.8) were associated with an increase in 7-y mortality. Time-dependently, these associations were stronger: SGA(4-5) (HR: 2.1; 95% CI: 1.7, 2.5) and SGA(1-3) (HR: 5.0; 95% CI: 3.8, 6.5). CONCLUSIONS: In dialysis patients, PEW at baseline assessed with SGA was associated with a 2-fold increased mortality risk in 7 y of follow-up. Time-dependently, this association was even stronger, which indicated that PEW was associated with a remarkably high risk of short-term mortality. These data imply that the 7-point SGA may validly distinguish different degrees of PEW associated with increasing risks of mortality.