Explore the vast range of titles available.

Background: Diagnostic and prognostic or predictive models serve different purposes. Whereas diagnostic models are usually used for classification, prognostic models incorporate the dimension of time, adding a stochastic element. Content: The ROC curve is typically used to evaluate clinical utility for both diagnostic and prognostic models. This curve assesses how well a test or model discriminates, or separates individuals into two classes, such as diseased and nondiseased. A strong risk predictor, such as lipids for cardiovascular disease, may have limited impact on the area under the curve, called the AUC or c-statistic, even if it alters predicted values. Calibration, measuring whether predicted probabilities agree with observed proportions, is another component of model accuracy important to assess. Reclassification can directly compare the clinical impact of two models by determining how many individuals would be reclassified into clinically relevant risk strata. For example, adding high-sensitivity C-reactive protein and family history to prediction models for cardiovascular disease using traditional risk factors moves approximately 30% of those at intermediate risk levels, such as 5%–10% or 10%–20% 10-year risk, into higher or lower risk categories, despite little change in the c-statistic. A calibration statistic can asses how well the new predicted values agree with those observed in the cross-classified data. Summary: Although it is useful for classification, evaluation of prognostic models should not rely solely on the ROC curve, but should assess both discrimination and calibration. Risk reclassification can aid in comparing the clinical impact of two models on risk for the individual, as well as the population.

Investigators measuring exposure biomarkers in urine typically adjust for creatinine to account for dilution-dependent sample variation in urine concentrations. Similarly, it is standard to adjust for serum lipids when measuring lipophilic chemicals in serum. However, there is controversy regarding the best approach, and existing methods may not effectively correct for measurement error. We compared adjustment methods, including novel approaches, using simulated case-control data. Using a directed acyclic graph framework, we defined six causal scenarios for epidemiologic studies of environmental chemicals measured in urine or serum. The scenarios include variables known to influence creatinine (e.g., age and hydration) or serum lipid levels (e.g., body mass index and recent fat intake). Over a range of true effect sizes, we analyzed each scenario using seven adjustment approaches and estimated the corresponding bias and confidence interval coverage across 1,000 simulated studies. For urinary biomarker measurements, our novel method, which incorporates both covariate-adjusted standardization and the inclusion of creatinine as a covariate in the regression model, had low bias and possessed 95% confidence interval coverage of nearly 95% for most simulated scenarios. For serum biomarker measurements, a similar approach involving standardization plus serum lipid level adjustment generally performed well. To control measurement error bias caused by variations in serum lipids or by urinary diluteness, we recommend improved methods for standardizing exposure concentrations across individuals.

AbstractObjectiveTo investigate whether vitamin D and marine derived long chain omega 3 fatty acids reduce autoimmune disease risk.DesignVitamin D and omega 3 trial (VITAL), a nationwide, randomized, double blind, placebo controlled trial with a two-by-two factorial design.SettingNationwide in the United States.Participants25 871 participants, consisting of 12 786 men ≥50 years and 13 085 women ≥55 years at enrollment.InterventionsVitamin D (2000 IU/day) or matched placebo, and omega 3 fatty acids (1000 mg/day) or matched placebo. Participants self-reported all incident autoimmune diseases from baseline to a median of 5.3 years of follow-up; these diseases were confirmed by extensive medical record review. Cox proportional hazard models were used to test the effects of vitamin D and omega 3 fatty acids on autoimmune disease incidence.Main outcome measuresThe primary endpoint was all incident autoimmune diseases confirmed by medical record review: rheumatoid arthritis, polymyalgia rheumatica, autoimmune thyroid disease, psoriasis, and all others.Results25 871 participants were enrolled and followed for a median of 5.3 years. 18 046 self-identified as non-Hispanic white, 5106 as black, and 2152 as other racial and ethnic groups. The mean age was 67.1 years. For the vitamin D arm, 123 participants in the treatment group and 155 in the placebo group had a confirmed autoimmune disease (hazard ratio 0.78, 95% confidence interval 0.61 to 0.99, P=0.05). In the omega 3 fatty acids arm, 130 participants in the treatment group and 148 in the placebo group had a confirmed autoimmune disease (0.85, 0.67 to 1.08, P=0.19). Compared with the reference arm (vitamin D placebo and omega 3 fatty acid placebo; 88 with confirmed autoimmune disease), 63 participants who received vitamin D and omega 3 fatty acids (0.69, 0.49 to 0.96), 60 who received only vitamin D (0.68, 0.48 to 0.94), and 67 who received only omega 3 fatty acids (0.74, 0.54 to 1.03) had confirmed autoimmune disease.ConclusionsVitamin D supplementation for five years, with or without omega 3 fatty acids, reduced autoimmune disease by 22%, while omega 3 fatty acid supplementation with or without vitamin D reduced the autoimmune disease rate by 15% (not statistically significant). Both treatment arms showed larger effects than the reference arm (vitamin D placebo and omega 3 fatty acid placebo).Study registrationClinicalTrials.gov NCT01351805 and NCT01169259

A study ancillary to a large trial showed that supplemental vitamin D 3 did not lower the risk of fractures among generally healthy midlife and older adults not selected for vitamin D deficiency, low bone mass, or osteoporosis.

The relation between sodium intake and cardiovascular disease is controversial. This study used individual-participant data from six prospective cohorts of healthy adults. Higher sodium and lower potassium intakes, estimated from multiple 24-hour urine samples, were associated in a dose-dependent manner with a higher cardiovascular risk.

Using a polygenic score of DNA sequence polymorphisms, the authors of this study quantified genetic risk and assessed four healthy lifestyle factors. Among participants at high genetic risk, a healthy lifestyle was associated with a reduced risk of coronary disease. Both genetic and lifestyle factors are key drivers of coronary artery disease, a complex disorder that is the leading cause of death worldwide. 1 A familial pattern in the risk of coronary artery disease was first described in 1938 and was subsequently confirmed in large studies involving twins and prospective cohorts. 2 – 6 Since 2007, genomewide association analyses have identified more than 50 independent loci associated with the risk of coronary artery disease. 7 – 15 These risk alleles, when aggregated into a polygenic risk score, are predictive of incident coronary events and provide a continuous and quantitative measure of genetic susceptibility. 16 – 24 Much . . .

This article reports the n−3 fatty acid portion of a randomized, placebo-controlled, two-by-two factorial trial of vitamin D and marine n−3 fatty acids in the primary prevention of cancer and cardiovascular disease. Fatty acids did not lead to a lower incidence of major cardiovascular events or cancer.

Mitochondrial DNA copy number (mtDNAcn) is a putative biomarker of oxidative stress and biological aging. Modifiable factors, including physical activity (PA), avoidance of heavy alcohol use and smoking, and maintaining good mental health, may reduce oxidative stress and promote healthy aging. Yet, limited data exist regarding how these factors are associated with mtDNAcn or whether age, sex or race/ethnicity moderate associations. In this cross-sectional study, we selected 391 adults (183 non-Hispanic White, 110 Black and 98 Hispanic; mean = 67 years) from the VITAL-DEP (VITamin D and OmegA-3 TriaL-Depression Endpoint Prevention) ancillary to the VITAL trial. We estimated associations between lifestyle and behavioral factors (PA, alcohol consumption, cigarette smoking and depression) and log-transformed mtDNAcn using multivariable linear regression models. MtDNAcn was not correlated with chronological age; women had ~17% higher mtDNAcn compared to men. There were no significant associations between PA measures (frequency, amount or intensity) or alcohol consumption with mtDNAcn. Cigarette smoking (per 5 pack-years) was significantly associated with mtDNAcn (percent difference = -2.9% (95% confidence interval (CI) = -5.4%, -0.4%)); a large contrast was observed among heavy vs. non-smokers ([greater than or equal to]30 vs. 0 pack-years): percent difference = -28.5% (95% CI = -44.2%, -8.3%). The estimate of mtDNAcn was suggestively different for past vs. no depression history (percent difference = -15.1% 95% CI = -30.8%, 4.1%), but this difference was not statistically significant. The association between smoking and log-mtDNAcn varied by sex and race/ethnicity; it was stronger in men and Black participants. While chance findings cannot be excluded, results from this study support associations of smoking, but not chronological age, with mtDNAcn and suggest nuanced considerations of mtDNAcn as indicative of varying oxidative stress states vs. biological aging itself.

Low vitamin D levels have been associated with cognitive decline; however, few randomized trials have been conducted. In a trial, we evaluated vitamin D3 supplementation on cognitive decline. We included participants aged 60+ years (mean[SD] = 70.9[5.8] years) free of cardiovascular disease and cancer in two substudies in the VITAL 2 × 2 randomized trial of vitamin D3 (2000 IU/day of cholecalciferol) and fish oil supplements: 3424 had cognitive assessments by phone (eight neuropsychologic tests; 2.8 years follow-up) and 794 had in-person assessments (nine tests; 2.0 years follow-up). The primary, pre-specified outcome was decline over two assessments in global composite score (average z-scores of all tests); substudy-specific results were meta-analyzed. The pooled mean difference in annual rate of decline (MD) for vitamin D3 versus placebo was 0.01 (95% CI − 0.01, 0.02; p = 0.39). We observed no interaction with baseline 25-hydroxyvitamin-D levels (p-interaction = 0.84) and a significant interaction with self-reported race (p-interaction = 0.01). Among Black participants (19%), those assigned vitamin D3 versus placebo had better cognitive maintenance (MD = 0.04, 95% CI 0.01, 0.08, similar to that observed for Black participants 1.2 years apart in age). Thus, vitamin D3 (2000 IU/day cholecalciferol) supplementation was not associated with cognitive decline over 2–3 years among community-dwelling older participants but may provide modest cognitive benefits in older Black adults, although these results need confirmation. Trial registration ClinicalTrials.gov; VITAL (NCT01169259), VITAL-DEP (NCT01696435) and VITAL-Cog (NCT01669915); the date the registration for the parent trial (NCT01169259) was submitted to the registry: 7/26/2010 and the date of first patient enrollment in either of the ancillary studies for cognitive function in a subset of eligible VITAL participants: 9/14/2011.

Testing for genetic effects on mean values of a quantitative trait has been a very successful strategy. However, most studies to date have not explored genetic effects on the variance of quantitative traits as a relevant consequence of genetic variation. In this report, we demonstrate that, under plausible scenarios of genetic interaction, the variance of a quantitative trait is expected to differ among the three possible genotypes of a biallelic SNP. Leveraging this observation with Levene's test of equality of variance, we propose a novel method to prioritize SNPs for subsequent gene-gene and gene-environment testing. This method has the advantageous characteristic that the interacting covariate need not be known or measured for a SNP to be prioritized. Using simulations, we show that this method has increased power over exhaustive search under certain conditions. We further investigate the utility of variance per genotype by examining data from the Women's Genome Health Study. Using this dataset, we identify new interactions between the LEPR SNP rs12753193 and body mass index in the prediction of C-reactive protein levels, between the ICAM1 SNP rs1799969 and smoking in the prediction of soluble ICAM-1 levels, and between the PNPLA3 SNP rs738409 and body mass index in the prediction of soluble ICAM-1 levels. These results demonstrate the utility of our approach and provide novel genetic insight into the relationship among obesity, smoking, and inflammation.