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      Causal associations between type 1 diabetes mellitus and cardiovascular diseases: a Mendelian randomization study

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          Abstract

          Background

          The presence of type 1 diabetes mellitus (T1DM) has been demonstrated to pose an increased risk for developing cardiovascular diseases (CVDs). However, the causal relationships between T1DM and CVDs remain unclear due to the uncontrolled confounding factors and reverse causation bias of the observational studies.

          Methods

          Summary statistics of T1DM and seven CVDs from the largest available genome-wide association studies (GWAS) of European ancestry and FinnGen biobank were extracted for the primary MR analysis, and the analysis was replicated using UK biobank (UKBB) for validation. Three complementary methods: inverse variance weighted (IVW), weighted median, and MR-Egger were used for the MR estimates. The potential pleiotropic effects were assessed by MR-Egger intercept and MR-PRESSO global test. Additionally, multivariable MR (MVMR) analysis was performed to examine whether T1DM has independent effects on CVDs with adjustment of potential confounding factors. Moreover, a two-step MR approach was used to assess the potential mediating effects of these factors on the causal effects between T1DM and CVDs.

          Results

          Causal effects of T1DM on peripheral atherosclerosis (odds ratio [OR] = 1.06, 95% confidence interval [CI]: 1.02–1.10; p = 0.002)] and coronary atherosclerosis (OR = 1.03, 95% CI: 1.01–1.05; p = 0.001) were found. The results were less likely to be biased by the horizontal pleiotropic effects (both p values of MR-Egger intercept and MR-PRESSO Global test > 0.05). In the following MVMR analysis, we found the causal effects of T1DM on peripheral atherosclerosis and coronary atherosclerosis remain significant after adjusting for a series of potential confounding factors. Moreover, we found that hypertension partly mediated the causal effects of T1DM on peripheral atherosclerosis (proportion of mediation effect in total effect: 11.47%, 95% CI: 3.23–19.71%) and coronary atherosclerosis (16.84%, 95% CI: 5.35–28.33%). We didn’t find significant causal relationships between T1DM and other CVDs, including heart failure (HF), coronary artery disease (CAD), atrial fibrillation (AF), myocardial infarction (MI) and stroke. For the reverse MR from CVD to T1DM, no significant causal relationships were identified.

          Conclusion

          This MR study provided evidence supporting the causal effect of T1DM on peripheral atherosclerosis and coronary atherosclerosis, with hypertension partly mediating this effect.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12933-023-01974-6.

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          Most cited references48

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          Mendelian Randomization Analysis With Multiple Genetic Variants Using Summarized Data

          Genome-wide association studies, which typically report regression coefficients summarizing the associations of many genetic variants with various traits, are potentially a powerful source of data for Mendelian randomization investigations. We demonstrate how such coefficients from multiple variants can be combined in a Mendelian randomization analysis to estimate the causal effect of a risk factor on an outcome. The bias and efficiency of estimates based on summarized data are compared to those based on individual-level data in simulation studies. We investigate the impact of gene–gene interactions, linkage disequilibrium, and ‘weak instruments’ on these estimates. Both an inverse-variance weighted average of variant-specific associations and a likelihood-based approach for summarized data give similar estimates and precision to the two-stage least squares method for individual-level data, even when there are gene–gene interactions. However, these summarized data methods overstate precision when variants are in linkage disequilibrium. If the P-value in a linear regression of the risk factor for each variant is less than , then weak instrument bias will be small. We use these methods to estimate the causal association of low-density lipoprotein cholesterol (LDL-C) on coronary artery disease using published data on five genetic variants. A 30% reduction in LDL-C is estimated to reduce coronary artery disease risk by 67% (95% CI: 54% to 76%). We conclude that Mendelian randomization investigations using summarized data from uncorrelated variants are similarly efficient to those using individual-level data, although the necessary assumptions cannot be so fully assessed.
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            2019 ESC Guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD

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              Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps

              We expanded GWAS discovery for type 2 diabetes (T2D) by combining data from 898,130 European-descent individuals (9% cases), after imputation to high-density reference panels. With these data, we (i) extend the inventory of T2D-risk variants (243 loci, 135 newly implicated in T2D predisposition, comprising 403 distinct association signals); (ii) enrich discovery of lower-frequency risk alleles (80 index variants with minor allele frequency 2); (iii) substantially improve fine-mapping of causal variants (at 51 signals, one variant accounted for >80% posterior probability of association (PPA)); (iv) extend fine-mapping through integration of tissue-specific epigenomic information (islet regulatory annotations extend the number of variants with PPA >80% to 73); (v) highlight validated therapeutic targets (18 genes with associations attributable to coding variants); and (vi) demonstrate enhanced potential for clinical translation (genome-wide chip heritability explains 18% of T2D risk; individuals in the extremes of a T2D polygenic risk score differ more than ninefold in prevalence).
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                Author and article information

                Contributors
                nkzc75@suda.edu.cn
                Journal
                Cardiovasc Diabetol
                Cardiovasc Diabetol
                Cardiovascular Diabetology
                BioMed Central (London )
                1475-2840
                2 September 2023
                2 September 2023
                2023
                : 22
                : 236
                Affiliations
                GRID grid.429222.d, ISNI 0000 0004 1798 0228, Department of Cardiology, , First Affiliated Hospital of Soochow University, ; No.188 Shizi Street, Gusu District, Suzhou City, Jiangsu Province China
                Article
                1974
                10.1186/s12933-023-01974-6
                10475187
                37659996
                708ea638-138b-4704-997d-0332d787607f
                © BioMed Central Ltd., part of Springer Nature 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 10 July 2023
                : 25 August 2023
                Funding
                Funded by: Suzhou Science and Technology Project
                Award ID: SKYD2022103
                Funded by: Suzhou Specialized Program for Diagnosis and Treatment Techniques of Clinical Key Diseases
                Award ID: LCZX202103
                Funded by: FundRef http://dx.doi.org/10.13039/501100007824, Soochow University;
                Award ID: P112206422
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2023

                Endocrinology & Diabetes
                mendelian randomization,type 1 diabetes mellitus,cardiovascular disease

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