Dataset Information

Genetic Predisposition to Coronary Artery Disease in Type 2 Diabetes Mellitus.

ABSTRACT:

Background

Coronary artery disease (CAD) is accelerated in subjects with type 2 diabetes mellitus (T2D).

Methods

To test whether this reflects differential genetic influences on CAD risk in subjects with T2D, we performed a systematic assessment of genetic overlap between CAD and T2D in 66 643 subjects (27 708 with CAD and 24 259 with T2D). Variants showing apparent association with CAD in stratified analyses or evidence of interaction were evaluated in a further 117 787 subjects (16 694 with CAD and 11 537 with T2D).

Results

None of the previously characterized CAD loci was found to have specific effects on CAD in T2D individuals, and a genome-wide interaction analysis found no new variants for CAD that could be considered T2D specific. When we considered the overall genetic correlations between CAD and its risk factors, we found no substantial differences in these relationships by T2D background.

Conclusions

This study found no evidence that the genetic architecture of CAD differs in those with T2D compared with those without T2D.

SUBMITTER: van Zuydam NR

PROVIDER: S-EPMC7748049 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Publications

Genetic Predisposition to Coronary Artery Disease in Type 2 Diabetes Mellitus.

van Zuydam Natalie R NR Ladenvall Claes C Voight Benjamin F BF Strawbridge Rona J RJ Fernandez-Tajes Juan J Rayner N William NW Robertson Neil R NR Mahajan Anubha A Vlachopoulou Efthymia E Goel Anuj A Kleber Marcus E ME Nelson Christopher P CP Kwee Lydia Coulter LC Esko Tõnu T Mihailov Evelin E Mägi Reedik R Milani Lili L Fischer Krista K Kanoni Stavroula S Kumar Jitender J Song Ci C Hartiala Jaana A JA Pedersen Nancy L NL Perola Markus M Gieger Christian C Peters Annette A Qu Liming L Willems Sara M SM Doney Alex S F ASF Morris Andrew D AD Zheng Yan Y Sesti Giorgio G Hu Frank B FB Qi Lu L Laakso Markku M Thorsteinsdottir Unnur U Grallert Harald H van Duijn Cornelia C Reilly Muredach P MP Ingelsson Erik E Deloukas Panos P Kathiresan Sek S Metspalu Andres A Shah Svati H SH Sinisalo Juha J Salomaa Veikko V Hamsten Anders A Samani Nilesh J NJ März Winfried W Hazen Stanley L SL Watkins Hugh H Saleheen Danish D Morris Andrew P AP Colhoun Helen M HM Groop Leif L McCarthy Mark I MI Palmer Colin N A CNA

Circulation. Genomic and precision medicine 20200813 6

<h4>Background</h4>Coronary artery disease (CAD) is accelerated in subjects with type 2 diabetes mellitus (T2D).<h4>Methods</h4>To test whether this reflects differential genetic influences on CAD risk in subjects with T2D, we performed a systematic assessment of genetic overlap between CAD and T2D in 66 643 subjects (27 708 with CAD and 24 259 with T2D). Variants showing apparent association with CAD in stratified analyses or evidence of interaction were evaluated in a further 117 787 subjects ...[more]

PMID: 33321069

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Project description:BackgroundCoronary artery disease (CAD) is the leading cause of morbidity and mortality in patients with type 2 diabetes mellitus (T2DM). The purpose of the present study was to discriminate the Indian CAD patients with or without T2DM by using multiple pathophysiological biomarkers.MethodsUsing sensitive multiplex protein assays, we assessed 46 protein markers including cytokines/chemokines, metabolic hormones, adipokines and apolipoproteins for evaluating different pathophysiological conditions of control, T2DM, CAD and T2DM with CAD patients (T2DM_CAD). Network analysis was performed to create protein-protein interaction networks by using significantly (p < 0.05) altered protein markers in each disease using STRING 10.5 database. We used two supervised analysis methods i.e., between class analysis (BCA) and principal component analysis (PCA) to reveals distinct biomarkers profiles. Further, random forest classification (RF) was used to classify the diseases by the panel of markers.ResultsOur two supervised analysis methods BCA and PCA revealed a distinct biomarker profiles and high degree of variability in the marker profiles for T2DM_CAD and CAD. Thereafter, the present study identified multiple potential biomarkers to differentiate T2DM, CAD, and T2DM_CAD patients based on their relative abundance in serum. RF classified T2DM based on the abundance patterns of nine markers i.e., IL-1β, GM-CSF, glucagon, PAI-I, rantes, IP-10, resistin, GIP and Apo-B; CAD by 14 markers i.e., resistin, PDGF-BB, PAI-1, lipocalin-2, leptin, IL-13, eotaxin, GM-CSF, Apo-E, ghrelin, adipsin, GIP, Apo-CII and IP-10; and T2DM _CAD by 12 markers i.e., insulin, resistin, PAI-1, adiponectin, lipocalin-2, GM-CSF, adipsin, leptin, Apo-AII, rantes, IL-6 and ghrelin with respect to the control subjects. Using network analysis, we have identified several cellular network proteins like PTPN1, AKT1, INSR, LEPR, IRS1, IRS2, IL1R2, IL6R, PCSK9 and MYD88, which are responsible for regulating inflammation, insulin resistance, and atherosclerosis.ConclusionWe have identified three distinct sets of serum markers for diabetes, CAD and diabetes associated with CAD in Indian patients using nonparametric-based machine learning approach. These multiple marker classifiers may be useful for monitoring progression from a healthy person to T2DM and T2DM to T2DM_CAD. However, these findings need to be further confirmed in the future studies with large number of samples.

Project description:BackgroundWhile type 2 diabetes mellitus (T2DM) is considered a putative causal risk factor for coronary artery disease (CAD), the intrinsic link underlying T2DM and CAD is not fully understood. We aimed to highlight the importance of integrated care targeting both diseases by investigating the phenotypic and genetic relationships between T2DM and CAD.MethodsWe evaluated phenotypic associations using data from the United Kingdom Biobank ( N = 472,050). We investigated genetic relationships by leveraging genomic data conducted in European ancestry for T2DM, with and without adjustment for body mass index (BMI) (T2DM: Ncase / Ncontrol = 74,124/824,006; T2DM adjusted for BMI [T2DM adj BMI]: Ncase / Ncontrol = 50,409/523,897) and for CAD ( Ncase / Ncontrol = 181,522/984,168). We performed additional analyses using genomic data conducted in multiancestry individuals for T2DM ( Ncase / Ncontrol = 180,834/1,159,055).ResultsObservational analysis suggested a bidirectional relationship between T2DM and CAD (T2DM→CAD: hazard ratio [HR] = 2.12, 95% confidence interval [CI]: 2.01-2.24; CAD→T2DM: HR = 1.72, 95% CI: 1.63-1.81). A positive overall genetic correlation between T2DM and CAD was observed ( rg = 0.39, P = 1.43 × 10 -75 ), which was largely independent of BMI (T2DM adj BMI-CAD: rg = 0.31, P = 1.20 × 10 -36 ). This was corroborated by six local signals, among which 9p21.3 showed the strongest genetic correlation. Cross-trait meta-analysis replicated 101 previously reported loci and discovered six novel pleiotropic loci. Mendelian randomization analysis supported a bidirectional causal relationship (T2DM→CAD: odds ratio [OR] = 1.13, 95% CI: 1.11-1.16; CAD→T2DM: OR = 1.12, 95% CI: 1.07-1.18), which was confirmed in multiancestry individuals (T2DM→CAD: OR = 1.13, 95% CI: 1.10-1.16; CAD→T2DM: OR = 1.08, 95% CI: 1.04-1.13). This bidirectional relationship was significantly mediated by systolic blood pressure and intake of 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors, with mediation proportions of 54.1% (95% CI: 24.9-83.4%) and 90.4% (95% CI: 29.3-151.5%), respectively.ConclusionOur observational and genetic analyses demonstrated an intrinsic bidirectional relationship between T2DM and CAD and clarified the biological mechanisms underlying this relationship.

Dataset Information

Genetic Predisposition to Coronary Artery Disease in Type 2 Diabetes Mellitus.

Background

Methods

Results

Conclusions

Publications

Genetic Predisposition to Coronary Artery Disease in Type 2 Diabetes Mellitus.

Similar Datasets

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