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      The effect of haptoglobin genotype on the association of asymmetric dimethylarginine and DDAH 1 polymorphism with diabetic macroangiopathy

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          Abstract

          Background

          Dimethylarginine dimethylaminohydrolase (DDAH) 1 maintains the bioavailability of nitric oxide by degrading asymmetric dimethylarginine (ADMA). Here, we aimed to investigate the effect of haptoglobin (Hp) genotype on the association of ADMA and DDAH 1 polymorphism with diabetic macroangiopathy.

          Methods

          In stage 1, 90 Chinese participants with type 2 diabetes were enrolled to measure a panel of targeted metabolites, including ADMA, using tandem mass spectrometry (BIOCRATES AbsoluteIDQ™ p180 kit). In stage 2, an independent cohort of 2965 Chinese patients with type 2 diabetes was recruited to analyze the effect of Hp genotype on the association between DDAH 1 rs233109 and diabetic macroangiopathy. Hp genotypes were detected using a validated assay based on the TaqMan method. DDAH 1 rs233109 was genotyped by matrix-assisted laser desorption/ionization time-of-flight mass spectroscopy using the MassARRAY platform.

          Results

          In stage 1, serum ADMA levels correlated with common Hp genotypes (β ± SE = − 0.049 ± 0.023, P = 0.035), but not with diabetic macroangiopathy ( P = 0.316). In stage 2, the distribution of DDAH 1 rs233109 genotype frequencies was 15% (CC), 47% (TC), and 38% (TT), which was in Hardy-Weinberg equilibrium ( P = 0.948). A significant Hp genotype by rs 233109 genotype interaction effect on diabetic macroangiopathy was found ( P = 0.017). After adjusting for confounders, patients homozygous for rs233109 CC were more likely to develop diabetic macroangiopathy than those carrying TT homozygotes in the Hp 2-2 subgroup [odds ratio = 1.750 (95% confidence interval, 1.101–2.783), P = 0.018].

          Conclusion

          Hp genotype affects the association between DDAH 1 rs233109 and diabetic macroangiopathy in Chinese patients with type 2 diabetes.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12933-022-01702-6.

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          Most cited references38

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          PLINK: a tool set for whole-genome association and population-based linkage analyses.

          Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
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            Type 2 diabetes.

            415 million people live with diabetes worldwide, and an estimated 193 million people have undiagnosed diabetes. Type 2 diabetes accounts for more than 90% of patients with diabetes and leads to microvascular and macrovascular complications that cause profound psychological and physical distress to both patients and carers and put a huge burden on health-care systems. Despite increasing knowledge regarding risk factors for type 2 diabetes and evidence for successful prevention programmes, the incidence and prevalence of the disease continues to rise globally. Early detection through screening programmes and the availability of safe and effective therapies reduces morbidity and mortality by preventing or delaying complications. Increased understanding of specific diabetes phenotypes and genotypes might result in more specific and tailored management of patients with type 2 diabetes, as has been shown in patients with maturity onset diabetes of the young. In this Seminar, we describe recent developments in the diagnosis and management of type 2 diabetes, existing controversies, and future directions of care.
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              Hardy-Weinberg Equilibrium Testing of Biological Ascertainment for Mendelian Randomization Studies

              Mendelian randomization (MR) permits causal inference between exposures and a disease. It can be compared with randomized controlled trials. Whereas in a randomized controlled trial the randomization occurs at entry into the trial, in MR the randomization occurs during gamete formation and conception. Several factors, including time since conception and sampling variation, are relevant to the interpretation of an MR test. Particularly important is consideration of the “missingness” of genotypes that can be originated by chance, genotyping errors, or clinical ascertainment. Testing for Hardy-Weinberg equilibrium (HWE) is a genetic approach that permits evaluation of missingness. In this paper, the authors demonstrate evidence of nonconformity with HWE in real data. They also perform simulations to characterize the sensitivity of HWE tests to missingness. Unresolved missingness could lead to a false rejection of causality in an MR investigation of trait-disease association. These results indicate that large-scale studies, very high quality genotyping data, and detailed knowledge of the life-course genetics of the alleles/genotypes studied will largely mitigate this risk. The authors also present a Web program (http://www.oege.org/software/hwe-mr-calc.shtml) for estimating possible missingness and an approach to evaluating missingness under different genetic models.

                Author and article information

                Contributors
                wpjia@sjtu.edu.cn
                alfredhc@sjtu.edu.cn
                Journal
                Cardiovasc Diabetol
                Cardiovasc Diabetol
                Cardiovascular Diabetology
                BioMed Central (London )
                1475-2840
                2 December 2022
                2 December 2022
                2022
                : 21
                : 265
                Affiliations
                [1 ]GRID grid.16821.3c, ISNI 0000 0004 0368 8293, Department of Endocrinology and Metabolism, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, , Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, ; 600 Yishan Road, Shanghai, 200233 People’s Republic of China
                [2 ]GRID grid.16821.3c, ISNI 0000 0004 0368 8293, Center for Translational Medicine, Shanghai Diabetes Institute, Department of Endocrinology and Metabolism, , Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, ; 600 Yishan Road, Shanghai, 200233 People’s Republic of China
                [3 ]Institute for Metabolic Disease, Fengxian Central Hospital Affiliated to Southern Medical University, 6600 Nanfeng Road, 201499 Shanghai, People’s Republic of China
                Author information
                http://orcid.org/0000-0002-6244-2168
                http://orcid.org/0000-0003-4314-2386
                Article
                1702
                10.1186/s12933-022-01702-6
                9716717
                fe19b502-2f88-4c6d-b641-bac2574cfde2
                © The Author(s) 2022

                Open AccessThis 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
                : 12 August 2022
                : 21 November 2022
                Funding
                Funded by: National Natural Science Foundation of China
                Award ID: 82000772
                Award ID: 81974118
                Funded by: Shanghai Sailing Program
                Award ID: 20YF1435700
                Funded by: Shuguang Project
                Award ID: 21SG11
                Funded by: Shanghai Outstanding Academic Leaders
                Award ID: 20XD1433300
                Funded by: Innovative research team of high-level local universities in Shanghai
                Award ID: SHSMU-ZDCX20212700
                Categories
                Research
                Custom metadata
                © The Author(s) 2022

                Endocrinology & Diabetes
                haptoglobin,dimethylarginine dimethylaminohydrolase,asymmetric dimethylarginine,single nucleotide polymorphism,diabetic macroangiopathy

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