426 research outputs found
Detecting clinically meaningful biomarkers with repeated measurements in an Electronic Health Record
Electronic health record (EHR) data are becoming an increasingly common data
source for understanding clinical risk of acute events. While their
longitudinal nature presents opportunities to observe changing risk over time,
these analyses are complicated by the sparse and irregular measurements of many
of the clinical metrics making typical statistical methods unsuitable for these
data. In this paper, we present an analytic procedure to both sample from an
EHR and analyze the data to detect clinically meaningful markers of acute
myocardial infarction (MI). Using an EHR from a large national dialysis
organization we abstracted the records of 64,318 individuals and identified
5,314 people that had an MI during the study period. We describe a nested
case-control design to sample appropriate controls and an analytic approach
using regression splines. Fitting a mixed-model with truncated power splines we
perform a series of goodness-of-fit tests to determine whether any of 11
regularly collected laboratory markers are useful clinical predictors. We test
the clinical utility of each marker using an independent test set. The results
suggest that EHR data can be easily used to detect markers of clinically acute
events. Special software or analytic tools are not needed, even with irregular
EHR data.Comment: 23 pages, 3 figure
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Urinary Albumin, Sodium, and Potassium and Cardiovascular Outcomes in the UK Biobank: Observational and Mendelian Randomization Analyses.
Urinary biomarkers are associated with cardiovascular disease, but the nature of these associations is not well understood. We performed multivariable-adjusted regression models to assess associations of random spot measurements of the urine sodium-potassium ratio (UNa/UK) and urine albumin adjusted for creatinine with cardiovascular risk factors, cardiovascular disease, and type 2 diabetes mellitus (T2D) in 478 311 participants of the UK Biobank. Further, we assessed the causal relationships of these kidney biomarkers, used as proxies for kidney function, with cardiovascular outcomes using the 2-sample Mendelian randomization approach. In observational analyses, UNa/UK showed significant inverse associations with atrial fibrillation, coronary artery disease, ischemic stroke, lipid-lowering medication, and T2D. In contrast, urine albumin adjusted for creatinine showed significant positive associations with atrial fibrillation, coronary artery disease, heart failure, hemorrhagic stroke, lipid-lowering medication, and T2D. We found a positive association between UNa/UK and albumin with blood pressure (BP), as well as with adiposity-related measures. After correcting for potential horizontal pleiotropy, we found evidence of causal associations of UNa/UK and albumin with BP (β systolic BP ≥2.63; β diastolic BP ≥0.85 SD increase in BP per SD change in UNa/UK and urine albumin adjusted for creatinine; P≤0.04), and of albumin with T2D (odds ratio=1.33 per SD change in albumin, P=0.02). Our comprehensive study of urinary biomarkers performed using state-of-the-art analyses of causality mirror and extend findings from randomized interventional trials which have established UNa/UK as a risk factor for hypertension. In addition, we detect a causal feedback loop between albumin and hypertension, and our finding of a bidirectional causal association between albumin and T2D reflects the well-known nephropathy in T2D
Rare coding variants in RCN3 are associated with blood pressure
Background: While large genome-wide association studies have identified nearly one thousand loci associated with variation in blood pressure, rare variant identification is still a challenge. In family-based cohorts, genome-wide linkage scans have been successful in identifying rare genetic variants for blood pressure. This study aims to identify low frequency and rare genetic variants within previously reported linkage regions on chromosomes 1 and 19 in African American families from the Trans-Omics for Precision Medicine (TOPMed) program. Genetic association analyses weighted by linkage evidence were completed with whole genome sequencing data within and across TOPMed ancestral groups consisting of 60,388 individuals of European, African, East Asian, Hispanic, and Samoan ancestries.
Results: Associations of low frequency and rare variants in RCN3 and multiple other genes were observed for blood pressure traits in TOPMed samples. The association of low frequency and rare coding variants in RCN3 was further replicated in UK Biobank samples (N = 403,522), and reached genome-wide significance for diastolic blood pressure (p = 2.01 × 10- 7).
Conclusions: Low frequency and rare variants in RCN3 contributes blood pressure variation. This study demonstrates that focusing association analyses in linkage regions greatly reduces multiple-testing burden and improves power to identify novel rare variants associated with blood pressure traits
Failure to replicate an association of SNPs in the oxidized LDL receptor gene (OLR1) with CAD
Abstract
Background
The lectin-like oxidized LDL receptor LOX-1 (encoded by OLR1) is believed to play a key role in atherogenesis and some reports suggest an association of OLR1 polymorphisms with myocardial infarction (MI). We tested whether single nucleotide polymorphisms (SNPs) in OLR1 are associated with clinically significant CAD in the Atherosclerotic Disease, VAscular FuNction, & Geneti C Epidemiology (ADVANCE) study.
Methods
ADVANCE is a population-based case-control study of subjects receiving care within Kaiser Permanente of Northern California including a subset of participants of the Coronary Artery Risk Development in Young Adults (CARDIA) study. We first resequenced the promoter, exonic, and splice site regions of OLR1 and then genotyped four single nucleotide polymorphisms (SNPs), including a non-synonymous SNP (rs11053646, Lys167Asn) as well as an intronic SNP (rs3736232) previously associated with CAD.
Results
In 1,809 cases with clinical CAD and 1,734 controls, the minor allele of the coding SNP was nominally associated with a lower odds ratio (OR) of CAD across all ethnic groups studied (minimally adjusted OR 0.8, P = 0.007; fully adjusted OR 0.8, P = 0.01). The intronic SNP was nominally associated with an increased risk of CAD (minimally adjusted OR 1.12, p = 0.03; fully adjusted OR 1.13, P = 0.03). However, these associations were not replicated in over 13,200 individuals (including 1,470 cases) in the Atherosclerosis Risk in Communities (ARIC) study.
Conclusion
Our results do not support the presence of an association between selected common SNPs in OLR1 and the risk of clinical CAD.http://deepblue.lib.umich.edu/bitstream/2027.42/112726/1/12881_2008_Article_317.pd
Characterizing the admixed African ancestry of African Americans
Genome-wide SNP analyses reveal the admixed African genetic ancestry of African Americans
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Leveraging population admixture to characterize the heritability of complex traits.
Despite recent progress on estimating the heritability explained by genotyped SNPs (h(2)g), a large gap between h(2)g and estimates of total narrow-sense heritability (h(2)) remains. Explanations for this gap include rare variants or upward bias in family-based estimates of h(2) due to shared environment or epistasis. We estimate h(2) from unrelated individuals in admixed populations by first estimating the heritability explained by local ancestry (h(2)γ). We show that h(2)γ = 2FSTCθ(1 - θ)h(2), where FSTC measures frequency differences between populations at causal loci and θ is the genome-wide ancestry proportion. Our approach is not susceptible to biases caused by epistasis or shared environment. We applied this approach to the analysis of 13 phenotypes in 21,497 African-American individuals from 3 cohorts. For height and body mass index (BMI), we obtained h(2) estimates of 0.55 ± 0.09 and 0.23 ± 0.06, respectively, which are larger than estimates of h(2)g in these and other data but smaller than family-based estimates of h(2)
Chromosome Xq23 Is Associated with Lower Atherogenic Lipid Concentrations and Favorable Cardiometabolic Indices
Autosomal genetic analyses of blood lipids have yielded key insights for coronary heart disease (CHD). However, X chromosome genetic variation is understudied for blood lipids in large sample sizes. We now analyze genetic and blood lipid data in a high-coverage whole X chromosome sequencing study of 65,322 multi-ancestry participants and perform replication among 456,893 European participants. Common alleles on chromosome Xq23 are strongly associated with reduced total cholesterol, LDL cholesterol, and triglycerides (min P = 8.5 × 10−72), with similar effects for males and females. Chromosome Xq23 lipid-lowering alleles are associated with reduced odds for CHD among 42,545 cases and 591,247 controls (P = 1.7 × 10−4), and reduced odds for diabetes mellitus type 2 among 54,095 cases and 573,885 controls (P = 1.4 × 10−5). Although we observe an association with increased BMI, waist-to-hip ratio adjusted for BMI is reduced, bioimpedance analyses indicate increased gluteofemoral fat, and abdominal MRI analyses indicate reduced visceral adiposity. Co-localization analyses strongly correlate increased CHRDL1 gene expression, particularly in adipose tissue, with reduced concentrations of blood lipids
Rare Coding Variants in RCN3 Are Associated with Blood Pressure
BACKGROUND: While large genome-wide association studies have identified nearly one thousand loci associated with variation in blood pressure, rare variant identification is still a challenge. In family-based cohorts, genome-wide linkage scans have been successful in identifying rare genetic variants for blood pressure. This study aims to identify low frequency and rare genetic variants within previously reported linkage regions on chromosomes 1 and 19 in African American families from the Trans-Omics for Precision Medicine (TOPMed) program. Genetic association analyses weighted by linkage evidence were completed with whole genome sequencing data within and across TOPMed ancestral groups consisting of 60,388 individuals of European, African, East Asian, Hispanic, and Samoan ancestries.
RESULTS: Associations of low frequency and rare variants in RCN3 and multiple other genes were observed for blood pressure traits in TOPMed samples. The association of low frequency and rare coding variants in RCN3 was further replicated in UK Biobank samples (N = 403,522), and reached genome-wide significance for diastolic blood pressure (p = 2.01 × 10− 7).
CONCLUSIONS: Low frequency and rare variants in RCN3 contributes blood pressure variation. This study demonstrates that focusing association analyses in linkage regions greatly reduces multiple-testing burden and improves power to identify novel rare variants associated with blood pressure traits
Plasma proteomic signatures of a direct measure of insulin sensitivity in two population cohorts
Aims/hypothesis:
The euglycemic hyperinsulinemic clamp (EIC) is a direct measure and the reference-standard in the assessment of whole-body insulin sensitivity but is laborious and expensive to perform. We aimed to assess the incremental value of high-throughput plasma proteomic profiling in
developing signatures correlating with the M-value derived from the EIC.
Methods:
We measured 828 proteins in the fasting plasma of 966 participants from the Relationship between Insulin Sensitivity and Cardiovascular disease (RISC) study and 745 participants from the Uppsala Longitudinal Study of Adult Men (ULSAM) using a high-throughput proximity extension assay. We used the least absolute shrinkage and selection operator (LASSO) approach using clinical variables and protein measures as features. Models were tested within and across cohorts. Our primary model performance metric was the proportion of the M-value variance explained (R2 82 ).
Results:
A standard LASSO model incorporating 53 proteins in addition to routinely available clinical variables increased the M-value R2 85 from 0.237 (95% confidence interval: 0.178-0.303) to 0.456 (0.372-0.536) in RISC. A similar pattern was observed in ULSAM in which the M-value R2 increased from 0.443 (0.360-0.530) to 0.632 (0.569-0.698) with the addition of 61 proteins. Models trained in one cohort and tested in the other also demonstrated significant improvements in R2 despite differences in baseline cohort characteristics and clamp methodology: RISC to ULSAM: 0.491 (0.433-0.539) for 51 proteins, ULSAM to RISC: 0.369 (0.331-0.416) for 67 proteins. A randomized LASSO and stability selection algorithm selected only two proteins per cohort (three unique proteins) which improved R2 92 but to a lesser degree than standard LASSO models: 0.352 (0.266-0.439) within RISC and 0.495 (0.404-0.585) within ULSAM. Differences in R2 93 explained between randomized and standard LASSO were notably reduced in the cross-cohort analyses despite the much smaller number of proteins selected: RISC to ULSAM range 0.444 (0.391-0.497) ULSAM to RISC range 0.348 (0.300-0.396). Models of proteins alone were as effective as models that included both clinical variables and proteins using either standard or randomized LASSO. The single most consistently selected protein across all analyses and models was IGFBP2.
Conclusions/interpretation:
A plasma proteomic signature identified through a standard LASSO approach improves the cross-sectional estimation of the M-value over routine clinical variables. However, a small subset of these proteins identified using stability selection algorithm affords much of this improvement especially when considering cross-cohort analyses. Our approach provides opportunities to improve the identification of insulin resistant individuals at risk of IR-related adverse health consequences
Methods in Genetics and Clinical Interpretation Randomized Trial of Personal Genomics for Preventive Cardiology Design and Challenges
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