scholarly journals Optimal Blood Pressure Thresholds for Minimal Coronary Artery Disease Risk in Type 1 Diabetes

Diabetes Care ◽  
2019 ◽  
Vol 42 (9) ◽  
pp. 1692-1699 ◽  
Author(s):  
Jingchuan Guo ◽  
Maria M. Brooks ◽  
Matthew F. Muldoon ◽  
Ashely I. Naimi ◽  
Trevor J. Orchard ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Coronary Artery Disease ◽  
Type 1 Diabetes ◽  
Coronary Artery ◽  
Disease Risk ◽  
Coronary Artery Disease Risk ◽  
Artery Disease ◽  
Optimal Blood Pressure

scholarly journals The role of coronary artery calcification testing in incident coronary artery disease risk prediction in type 1 diabetes

Diabetologia ◽  
2018 ◽  
Vol 62 (2) ◽  
pp. 259-268 ◽  
Author(s):  
Jingchuan Guo ◽  
Sebhat A. Erqou ◽  
Rachel G. Miller ◽  
Daniel Edmundowicz ◽  
Trevor J. Orchard ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Type 1 Diabetes ◽  
Coronary Artery ◽  
Risk Prediction ◽  
Coronary Artery Calcification ◽  
Disease Risk ◽  
Coronary Artery Disease Risk ◽  
Artery Disease

Data driven patterns of nutrient intake and coronary artery disease risk in adults with type 1 diabetes

2021 ◽  
pp. 108016
Author(s):  
Susan M. Devaraj ◽  
Rachel G. Miller ◽  
Trevor J. Orchard ◽  
Andrea M. Kriska ◽  
Tiffany Gary-Webb ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Type 1 Diabetes ◽  
Coronary Artery ◽  
Nutrient Intake ◽  
Disease Risk ◽  
Data Driven ◽  
Coronary Artery Disease Risk ◽  
Artery Disease

Predictive Utilities of Blood Pressure Variables on Coronary Artery Disease in Type 1 Diabetes

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 161-OR
Author(s):  
JINGCHUAN GUO ◽  
TREVOR J. ORCHARD
Keyword(s):  
Blood Pressure ◽  
Coronary Artery Disease ◽  
Type 1 Diabetes ◽  
Coronary Artery ◽  
Artery Disease

scholarly journals MR-Clust: Clustering of genetic variants in Mendelian randomization with similar causal estimates

2019 ◽  
Author(s):  
Christopher N Foley ◽  
Paul D W Kirk ◽  
Stephen Burgess
Keyword(s):  
Blood Pressure ◽  
Coronary Artery Disease ◽  
Risk Factor ◽  
Coronary Artery ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Disease Risk ◽  
Causal Effect ◽  
Coronary Artery Disease Risk ◽  
Artery Disease

AbstractMotivationMendelian randomization is an epidemiological technique that uses genetic variants as instrumental variables to estimate the causal effect of a risk factor on an outcome. We consider a scenario in which causal estimates based on each variant in turn differ more strongly than expected by chance alone, but the variants can be divided into distinct clusters, such that all variants in the cluster have similar causal estimates. This scenario is likely to occur when there are several distinct causal mechanisms by which a risk factor influences an outcome with different magnitudes of causal effect. We have developed an algorithm MR-Clust that finds such clusters of variants, and so can identify variants that reflect distinct causal mechanisms. Two features of our clustering algorithm are that it accounts for uncertainty in the causal estimates, and it includes ‘null’ and ‘junk’ clusters, to provide protection against the detection of spurious clusters.ResultsOur algorithm correctly detected the number of clusters in a simulation analysis, outperforming the popular Mclust method. In an applied example considering the effect of blood pressure on coronary artery disease risk, the method detected four clusters of genetic variants. A hypothesis-free search suggested that variants in the cluster with a negative effect of blood pressure on coronary artery disease risk were more strongly related to trunk fat percentage and other adiposity measures than variants not in this cluster.Availability and ImplementationMR-Clust can be downloaded from https://github.com/cnfoley/[email protected] or [email protected] InformationSupplementary Material is included in the submission.

scholarly journals MR-Clust: clustering of genetic variants in Mendelian randomization with similar causal estimates

2020 ◽  
Author(s):  
Christopher N Foley ◽  
Amy M Mason ◽  
Paul D W Kirk ◽  
Stephen Burgess
Keyword(s):  
Blood Pressure ◽  
Coronary Artery Disease ◽  
Risk Factor ◽  
Coronary Artery ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Disease Risk ◽  
Causal Effect ◽  
Coronary Artery Disease Risk ◽  
Artery Disease

Abstract Motivation Mendelian randomization is an epidemiological technique that uses genetic variants as instrumental variables to estimate the causal effect of a risk factor on an outcome. We consider a scenario in which causal estimates based on each variant in turn differ more strongly than expected by chance alone, but the variants can be divided into distinct clusters, such that all variants in the cluster have similar causal estimates. This scenario is likely to occur when there are several distinct causal mechanisms by which a risk factor influences an outcome with different magnitudes of causal effect. We have developed an algorithm MR-Clust that finds such clusters of variants, and so can identify variants that reflect distinct causal mechanisms. Two features of our clustering algorithm are that it accounts for differential uncertainty in the causal estimates, and it includes ‘null’ and ‘junk’ clusters, to provide protection against the detection of spurious clusters. Results Our algorithm correctly detected the number of clusters in a simulation analysis, outperforming methods that either do not account for uncertainty or do not include null and junk clusters. In an applied example considering the effect of blood pressure on coronary artery disease risk, the method detected four clusters of genetic variants. A post hoc hypothesis-generating search suggested that variants in the cluster with a negative effect of blood pressure on coronary artery disease risk were more strongly related to trunk fat percentage and other adiposity measures than variants not in this cluster. Availability and implementation MR-Clust can be downloaded from https://github.com/cnfoley/mrclust. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Genetic Predisposition to Higher Blood Pressure Increases Coronary Artery Disease Risk

Hypertension ◽  
2013 ◽  
Vol 61 (5) ◽  
pp. 995-1001 ◽  
Author(s):  
Wolfgang Lieb ◽  
Henning Jansen ◽  
Christina Loley ◽  
Michael J. Pencina ◽  
Christopher P. Nelson ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Genetic Predisposition ◽  
Disease Risk ◽  
Coronary Artery Disease Risk ◽  
Artery Disease

scholarly journals Prognostic Significance of Pulse Pressure and Other Blood Pressure Components for Coronary Artery Disease in Type 1 Diabetes

2019 ◽  
Vol 32 (11) ◽  
pp. 1075-1081 ◽  
Author(s):  
Jingchuan Guo ◽  
Matthew F Muldoon ◽  
Maria M Brooks ◽  
Trevor J Orchard ◽  
Tina Costacou
Keyword(s):  
Blood Pressure ◽  
Coronary Artery Disease ◽  
Type 1 Diabetes ◽  
Coronary Artery ◽  
Mean Arterial Pressure ◽  
Arterial Pressure ◽  
Diastolic Blood Pressure ◽  
Pulse Pressure ◽  
Artery Disease

AbstractBACKGROUNDTo compare in individuals with type 1 diabetes the prediction of incident coronary artery disease (CAD) by components of resting blood pressure—systolic, diastolic, pulse pressure, and mean arterial pressure.METHODSIn 605 participants without known CAD at baseline and followed sequentially for 25 years, we used Cox modeling built for each blood pressure component associated with incident CAD, overall and stratified by age (<35 and ≥35 years) or hemoglobin A1c (HbA1c) (<9% and ≥9%).RESULTSBaseline mean age and diabetes duration were 27 and 19 years, respectively. We observed an early asymptote and then fall in diastolic blood pressure in their late 30s and early 40s in this group of type 1 diabetes individuals, followed by an early rise of pulse pressure. Adjusted hazard ratios (HR) (95% con) for CAD associated with 1 SD pressure increase were 1.35 (1.17, 1.56) for systolic pressure; 1.30 (1.12, 1.51) for diastolic pressure; 1.20 (1.03, 1.39) for pulse pressure; and 1.35 (1.17, 1.56) for mean arterial pressure. Pulse pressure emerged as a strong predictor of CAD at age ≥ 35 years (HR: 1.49 [1.15, 1.94]) and for HbA1c ≥ 9% (HR: 1.32 [1.01, 1.72]).CONCLUSIONSIndividuals with type 1 diabetes may manifest early vascular aging by an early decline in diastolic blood pressure and rise in pulse pressure, the latter parameter becoming a comparable to systolic blood pressure in predictor incident CAD in those aged over 35 years and those with poor glycemic control.

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