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Article: Genome-wide association study of serum magnesium in type 2 diabetes.

Oost, Lynette J / Slieker, Roderick C / Blom, Marieke T / 't Hart, Leen M / Hoenderop, Joost G J / Beulens, Joline W J / de Baaij, Jeroen H F

Genes & nutrition

2024 Volume 19, Issue 1, Page(s) 2

Abstract: People with type 2 diabetes have a tenfold higher prevalence of hypomagnesemia, which is suggested to be caused by low dietary magnesium intake, medication use, and genetics. This study aims to identify the genetic loci that influence serum magnesium ... ...

Abstract	People with type 2 diabetes have a tenfold higher prevalence of hypomagnesemia, which is suggested to be caused by low dietary magnesium intake, medication use, and genetics. This study aims to identify the genetic loci that influence serum magnesium concentration in 3466 people with type 2 diabetes. The GWAS models were adjusted for age, sex, eGFR, and HbA1c. Associated traits were identified using publicly available data from GTEx consortium, a human kidney eQTL atlas, and the Open GWAS database. The GWAS identified a genome-wide significant locus in TAF3 (p = 2.9 × 10
Language	English
Publishing date	2024-01-26
Publishing country	Germany
Document type	Journal Article
ZDB-ID	2416599-2
ISSN	1865-3499 ; 1555-8932
ISSN (online)	1865-3499
ISSN	1555-8932
DOI	10.1186/s12263-024-00738-5
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Article ; Online: Trajectories of clinical characteristics, complications and treatment choices in data-driven subgroups of type 2 diabetes.

Li, Xinyu / Donnelly, Louise A / Slieker, Roderick C / Beulens, Joline W J / 't Hart, Leen M / Elders, Petra J M / Pearson, Ewan R / van Giessen, Anoukh / Leal, Jose / Feenstra, Talitha

Diabetologia

2024

Abstract: Aims/hypothesis: This study aimed to explore the added value of subgroups that categorise individuals with type 2 diabetes by k-means clustering for two primary care registries (the Netherlands and Scotland), inspired by Ahlqvist's novel diabetes ... ...

Abstract	Aims/hypothesis: This study aimed to explore the added value of subgroups that categorise individuals with type 2 diabetes by k-means clustering for two primary care registries (the Netherlands and Scotland), inspired by Ahlqvist's novel diabetes subgroups and previously analysed by Slieker et al. METHODS: We used two Dutch and Scottish diabetes cohorts (N=3054 and 6145; median follow-up=11.2 and 12.3 years, respectively) and defined five subgroups by k-means clustering with age at baseline, BMI, HbA Results: Subgroups' risk factors were significantly different, and these differences remained generally consistent over follow-up. Among all subgroups, individuals with severe insulin resistance faced a significantly higher risk of myocardial infarction both before (HR 1.65; 95% CI 1.40, 1.94) and after adjusting for age effect (HR 1.72; 95% CI 1.46, 2.02) compared with mild diabetes with high HDL-cholesterol. Individuals with severe insulin-deficient diabetes were most intensively treated, with more than 25% prescribed insulin at 10 years of diagnosis. For severe insulin-deficient diabetes relative to mild diabetes, the relative risks for using insulin relative to no common treatment would be expected to increase by a factor of 3.07 (95% CI 2.73, 3.44), holding other factors constant. Clustering indicators were better predictors of progression variation relative to subgroups, but prediction accuracy may improve after combining both. Clusters were consistent over 8 years with an accuracy ranging from 59% to 72%. Conclusions/interpretation: Data-driven subgroup allocations were generally consistent over follow-up and captured significant differences in risk factor trajectories, medication patterns and complication risks. Subgroups serve better as a complement rather than as a basis for compressing clustering indicators.
Language	English
Publishing date	2024-04-16
Publishing country	Germany
Document type	Journal Article
ZDB-ID	1694-9
ISSN	1432-0428 ; 0012-186X
ISSN (online)	1432-0428
ISSN	0012-186X
DOI	10.1007/s00125-024-06147-y
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Article ; Online: Diabetes risk loci-associated pathways are shared across metabolic tissues.

Bouland, Gerard A / Beulens, Joline W J / Nap, Joey / van der Slik, Arno R / Zaldumbide, Arnaud / 't Hart, Leen M / Slieker, Roderick C

BMC genomics

2022 Volume 23, Issue 1, Page(s) 368

Abstract: Aims/hypothesis: Numerous genome-wide association studies have been performed to understand the influence of genetic variation on type 2 diabetes etiology. Many identified risk variants are located in non-coding and intergenic regions, which complicates ...

Abstract	Aims/hypothesis: Numerous genome-wide association studies have been performed to understand the influence of genetic variation on type 2 diabetes etiology. Many identified risk variants are located in non-coding and intergenic regions, which complicates understanding of how genes and their downstream pathways are influenced. An integrative data approach will help to understand the mechanism and consequences of identified risk variants. Methods: In the current study we use our previously developed method CONQUER to overlap 403 type 2 diabetes risk variants with regulatory, expression and protein data to identify tissue-shared disease-relevant mechanisms. Results: One SNP rs474513 was found to be an expression-, protein- and metabolite QTL. Rs474513 influenced LPA mRNA and protein levels in the pancreas and plasma, respectively. On the pathway level, in investigated tissues most SNPs linked to metabolism. However, in eleven of the twelve tissues investigated nine SNPs were linked to differential expression of the ribosome pathway. Furthermore, seven SNPs were linked to altered expression of genes linked to the immune system. Among them, rs601945 was found to influence multiple HLA genes, including HLA-DQA2, in all twelve tissues investigated. Conclusion: Our results show that in addition to the classical metabolism pathways, other pathways may be important to type 2 diabetes that show a potential overlap with type 1 diabetes.
MeSH term(s)	Diabetes Mellitus, Type 1/genetics ; Diabetes Mellitus, Type 2/genetics ; Genetic Predisposition to Disease ; Genome-Wide Association Study ; Humans ; Polymorphism, Single Nucleotide
Language	English
Publishing date	2022-05-14
Publishing country	England
Document type	Journal Article
ZDB-ID	2041499-7
ISSN	1471-2164 ; 1471-2164
ISSN (online)	1471-2164
ISSN	1471-2164
DOI	10.1186/s12864-022-08587-5
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Article ; Online: An omics-based machine learning approach to predict diabetes progression: a RHAPSODY study.

Slieker, Roderick C / Münch, Magnus / Donnelly, Louise A / Bouland, Gerard A / Dragan, Iulian / Kuznetsov, Dmitry / Elders, Petra J M / Rutter, Guy A / Ibberson, Mark / Pearson, Ewan R / 't Hart, Leen M / van de Wiel, Mark A / Beulens, Joline W J

Diabetologia

2024 Volume 67, Issue 5, Page(s) 885–894

Abstract: Aims/hypothesis: People with type 2 diabetes are heterogeneous in their disease trajectory, with some progressing more quickly to insulin initiation than others. Although classical biomarkers such as age, HbA: Methods: In two prospective cohort ... ...

Abstract	Aims/hypothesis: People with type 2 diabetes are heterogeneous in their disease trajectory, with some progressing more quickly to insulin initiation than others. Although classical biomarkers such as age, HbA Methods: In two prospective cohort studies as part of IMI-RHAPSODY, we investigated whether clinical variables and three types of molecular markers (metabolites, lipids, proteins) can predict time to insulin requirement using different machine learning approaches (lasso, ridge, GRridge, random forest). Clinical variables included age, sex, HbA Results: Of the 585 individuals from the Hoorn Diabetes Care System (DCS) cohort, 69 required insulin during follow-up (1.0-11.4 years); of the 571 individuals in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) cohort, 175 required insulin during follow-up (0.3-11.8 years). Overall, the clinical variables and proteins were selected in the different models most often, followed by the metabolites. The most frequently selected clinical variables were HbA Conclusions/interpretation: Using machine learning approaches, we show that insulin requirement risk can be modestly well predicted by predominantly clinical variables. Inclusion of molecular markers improves the prognostic performance beyond that of clinical variables by up to 5%. Such prognostic models could be useful for identifying people with diabetes at high risk of progressing quickly to treatment intensification. Data availability: Summary statistics of lipidomic, proteomic and metabolomic data are available from a Shiny dashboard at https://rhapdata-app.vital-it.ch .
MeSH term(s)	Humans ; Diabetes Mellitus, Type 2/metabolism ; Prospective Studies ; C-Peptide ; Proteomics ; Insulin/therapeutic use ; Biomarkers ; Machine Learning ; Cholesterol
Chemical Substances	C-Peptide ; Insulin ; Biomarkers ; Cholesterol (97C5T2UQ7J)
Language	English
Publishing date	2024-02-19
Publishing country	Germany
Document type	Journal Article
ZDB-ID	1694-9
ISSN	1432-0428 ; 0012-186X
ISSN (online)	1432-0428
ISSN	0012-186X
DOI	10.1007/s00125-024-06105-8
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ab Jg. 2022: Lesesaal (EG)

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Article ; Online: Apolipoprotein-CIII

Naber, Annemieke / Demus, Daniel / Slieker, Roderick / Nicolardi, Simone / Beulens, Joline W J / Elders, Petra J M / Lieverse, Aloysius G / Sijbrands, Eric J G / 't Hart, Leen M / Wuhrer, Manfred / van Hoek, Mandy

International journal of molecular sciences

2023 Volume 24, Issue 19

Abstract: Apolipoprotein-CIII (apo-CIII) is involved in triglyceride-rich lipoprotein metabolism and linked to beta-cell damage, insulin resistance, and cardiovascular disease. Apo-CIII exists in four main proteoforms: non-glycosylated (apo- ... ...

Abstract	Apolipoprotein-CIII (apo-CIII) is involved in triglyceride-rich lipoprotein metabolism and linked to beta-cell damage, insulin resistance, and cardiovascular disease. Apo-CIII exists in four main proteoforms: non-glycosylated (apo-CIII
MeSH term(s)	Humans ; Apolipoprotein C-III/genetics ; Apolipoproteins C/genetics ; Diabetes Mellitus, Type 2/genetics ; Glycosylation ; Genome-Wide Association Study ; Triglycerides ; Hypertriglyceridemia ; Hyperlipidemias ; Cholesterol, HDL ; Receptors, Cytoplasmic and Nuclear/genetics ; Vesicular Transport Proteins/genetics ; Cytoskeletal Proteins/genetics ; Adaptor Proteins, Signal Transducing/genetics
Chemical Substances	Apolipoprotein C-III ; Apolipoproteins C ; Triglycerides ; Cholesterol, HDL ; NRBP1 protein, human ; Receptors, Cytoplasmic and Nuclear ; Vesicular Transport Proteins ; IFT172 protein, human ; Cytoskeletal Proteins ; Adaptor Proteins, Signal Transducing
Language	English
Publishing date	2023-10-02
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2019364-6
ISSN	1422-0067 ; 1422-0067 ; 1661-6596
ISSN (online)	1422-0067
ISSN	1422-0067 ; 1661-6596
DOI	10.3390/ijms241914844
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Article ; Online: Potential Value of Identifying Type 2 Diabetes Subgroups for Guiding Intensive Treatment: A Comparison of Novel Data-Driven Clustering With Risk-Driven Subgroups.

Li, Xinyu / van Giessen, Anoukh / Altunkaya, James / Slieker, Roderick C / Beulens, Joline W J / 't Hart, Leen M / Pearson, Ewan R / Elders, Petra J M / Feenstra, Talitha L / Leal, Jose

Diabetes care

2023 Volume 46, Issue 7, Page(s) 1395–1403

Abstract: Objective: To estimate the impact on lifetime health and economic outcomes of different methods of stratifying individuals with type 2 diabetes, followed by guideline-based treatment intensification targeting BMI and LDL in addition to HbA1c.: ... ...

Abstract	Objective: To estimate the impact on lifetime health and economic outcomes of different methods of stratifying individuals with type 2 diabetes, followed by guideline-based treatment intensification targeting BMI and LDL in addition to HbA1c. Research design and methods: We divided 2,935 newly diagnosed individuals from the Hoorn Diabetes Care System (DCS) cohort into five Risk Assessment and Progression of Diabetes (RHAPSODY) data-driven clustering subgroups (based on age, BMI, HbA1c, C-peptide, and HDL) and four risk-driven subgroups by using fixed cutoffs for HbA1c and risk of cardiovascular disease based on guidelines. The UK Prospective Diabetes Study Outcomes Model 2 estimated discounted expected lifetime complication costs and quality-adjusted life-years (QALYs) for each subgroup and across all individuals. Gains from treatment intensification were compared with care as usual as observed in DCS. A sensitivity analysis was conducted based on Ahlqvist subgroups. Results: Under care as usual, prognosis in the RHAPSODY data-driven subgroups ranged from 7.9 to 12.6 QALYs. Prognosis in the risk-driven subgroups ranged from 6.8 to 12.0 QALYs. Compared with homogenous type 2 diabetes, treatment for individuals in the high-risk subgroups could cost 22.0% and 25.3% more and still be cost effective for data-driven and risk-driven subgroups, respectively. Targeting BMI and LDL in addition to HbA1c might deliver up to 10-fold increases in QALYs gained. Conclusions: Risk-driven subgroups better discriminated prognosis. Both stratification methods supported stratified treatment intensification, with the risk-driven subgroups being somewhat better in identifying individuals with the most potential to benefit from intensive treatment. Irrespective of stratification approach, better cholesterol and weight control showed substantial potential for health gains.
MeSH term(s)	Humans ; Diabetes Mellitus, Type 2/drug therapy ; Glycated Hemoglobin ; Prospective Studies ; Cholesterol ; Cluster Analysis ; Cost-Benefit Analysis ; Quality-Adjusted Life Years
Chemical Substances	Glycated Hemoglobin ; Cholesterol (97C5T2UQ7J)
Language	English
Publishing date	2023-05-01
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	441231-x
ISSN	1935-5548 ; 0149-5992
ISSN (online)	1935-5548
ISSN	0149-5992
DOI	10.2337/dc22-2170
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Zs.A 1434: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (1.OG) ab Jg. 2022: Lesesaal (EG)
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Article ; Online: Altered blood gene expression in the obesity-related type 2 diabetes cluster may be causally involved in lipid metabolism: a Mendelian randomisation study.

de Klerk, Juliette A / Beulens, Joline W J / Mei, Hailiang / Bijkerk, Roel / van Zonneveld, Anton Jan / Koivula, Robert W / Elders, Petra J M / 't Hart, Leen M / Slieker, Roderick C

Diabetologia

2023 Volume 66, Issue 6, Page(s) 1057–1070

Abstract: Aims/hypothesis: The aim of this study was to identify differentially expressed long non-coding RNAs (lncRNAs) and mRNAs in whole blood of people with type 2 diabetes across five different clusters: severe insulin-deficient diabetes (SIDD), severe ... ...

Abstract	Aims/hypothesis: The aim of this study was to identify differentially expressed long non-coding RNAs (lncRNAs) and mRNAs in whole blood of people with type 2 diabetes across five different clusters: severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), mild diabetes (MD) and mild diabetes with high HDL-cholesterol (MDH). This was to increase our understanding of different molecular mechanisms underlying the five putative clusters of type 2 diabetes. Methods: Participants in the Hoorn Diabetes Care System (DCS) cohort were clustered based on age, BMI, HbA Results: Eleven lncRNAs and 175 mRNAs were differentially expressed in the MOD cluster, the lncRNA AL354696.2 was upregulated in the SIDD cluster and GPR15 mRNA was downregulated in the MDH cluster. mRNAs and lncRNAs that were differentially expressed in the MOD cluster were correlated among each other. Six lncRNAs and 120 mRNAs validated in the IMI DIRECT study. Using two-sample Mendelian randomisation, we found 52 mRNAs to have a causal effect on anthropometric traits (n=23) and lipid metabolism traits (n=10). GPR146 showed a causal effect on plasma HDL-cholesterol levels (p = 2×10 Conclusions/interpretation: Multiple lncRNAs and mRNAs were found to be differentially expressed among clusters and particularly in the MOD cluster. mRNAs in the MOD cluster showed a possible causal effect on anthropometric traits, lipid metabolism traits and blood cell fractions. Together, our results show that individuals in the MOD cluster show aberrant RNA expression of genes that have a suggested causal role on multiple diabetes-relevant traits.
MeSH term(s)	Humans ; Diabetes Mellitus, Type 2/genetics ; Lipid Metabolism/genetics ; RNA, Long Noncoding/genetics ; RNA, Long Noncoding/metabolism ; Genome-Wide Association Study ; Cholesterol, HDL ; Gene Expression ; Insulins ; Obesity/complications ; Obesity/genetics ; Receptors, Peptide/genetics ; Receptors, Peptide/metabolism ; Receptors, G-Protein-Coupled/metabolism
Chemical Substances	RNA, Long Noncoding ; Cholesterol, HDL ; Insulins ; GPR15 protein, human ; Receptors, Peptide ; Receptors, G-Protein-Coupled
Language	English
Publishing date	2023-02-24
Publishing country	Germany
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1694-9
ISSN	1432-0428 ; 0012-186X
ISSN (online)	1432-0428
ISSN	0012-186X
DOI	10.1007/s00125-023-05886-8
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Article ; Online: IgG N-glycans are associated with prevalent and incident complications of type 2 diabetes.

Memarian, Elham / Heijmans, Ralph / Slieker, Roderick C / Sierra, Adriana / Gornik, Olga / Beulens, Joline W J / Hanic, Maja / Elders, Petra / Pascual, Julio / Sijbrands, Eric / Lauc, Gordan / Dotz, Viktoria / Barrios, Clara / 't Hart, Leen M / Wuhrer, Manfred / van Hoek, Mandy

Diabetes/metabolism research and reviews

2023 Volume 39, Issue 7, Page(s) e3685

Abstract: Aims/hypothesis: Inflammation is important in the development of type 2 diabetes complications. The N-glycosylation of IgG influences its role in inflammation. To date, the association of plasma IgG N-glycosylation with type 2 diabetes complications has ...

Abstract	Aims/hypothesis: Inflammation is important in the development of type 2 diabetes complications. The N-glycosylation of IgG influences its role in inflammation. To date, the association of plasma IgG N-glycosylation with type 2 diabetes complications has not been extensively investigated. We hypothesised that N-glycosylation of IgG may be related to the development of complications of type 2 diabetes. Methods: In three independent type 2 diabetes cohorts, plasma IgG N-glycosylation was measured using ultra performance liquid chromatography (DiaGene n = 1815, GenodiabMar n = 640) and mass spectrometry (Hoorn Diabetes Care Study n = 1266). We investigated the associations of IgG N-glycosylation (fucosylation, galactosylation, sialylation and bisection) with incident and prevalent nephropathy, retinopathy and macrovascular disease using Cox- and logistic regression, followed by meta-analyses. The models were adjusted for age and sex and additionally for clinical risk factors. Results: IgG galactosylation was negatively associated with prevalent and incident nephropathy and macrovascular disease after adjustment for clinical risk factors. Sialylation was negatively associated with incident diabetic nephropathy after adjustment for clinical risk factors. For incident retinopathy, similar associations were found for galactosylation, adjusted for age and sex. Conclusions: We showed that IgG N-glycosylation, particularly galactosylation and to a lesser extent sialylation, is associated with a higher prevalence and future development of macro- and microvascular complications of diabetes. These findings indicate the predictive potential of IgG N-glycosylation in diabetes complications and should be analysed further in additional large cohorts to obtain the power to solidify these conclusions.
Language	English
Publishing date	2023-07-09
Publishing country	England
Document type	Journal Article
ZDB-ID	1470192-3
ISSN	1520-7560 ; 1520-7552
ISSN (online)	1520-7560
ISSN	1520-7552
DOI	10.1002/dmrr.3685
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Article ; Online: Assessment of the bi-directional relationship between blood mitochondrial DNA copy number and type 2 diabetes mellitus: a multivariable-adjusted regression and Mendelian randomisation study.

Wang, Wenyi / Luo, Jiao / Willems van Dijk, Ko / Hägg, Sara / Grassmann, Felix / T Hart, Leen M / van Heemst, Diana / Noordam, Raymond

Diabetologia

2022 Volume 65, Issue 10, Page(s) 1676–1686

Abstract: Aims/hypothesis: Mitochondrial dysfunction, which can be approximated by blood mitochondrial DNA copy number (mtDNA-CN), has been implicated in the pathogenesis of type 2 diabetes mellitus. Thus far, however, insights from prospective cohort studies and ...

Abstract	Aims/hypothesis: Mitochondrial dysfunction, which can be approximated by blood mitochondrial DNA copy number (mtDNA-CN), has been implicated in the pathogenesis of type 2 diabetes mellitus. Thus far, however, insights from prospective cohort studies and Mendelian randomisation (MR) analyses on this relationship are limited. We assessed the association between blood mtDNA-CN and incident type 2 diabetes using multivariable-adjusted regression analyses, and the associations between blood mtDNA-CN and type 2 diabetes and BMI using bi-directional MR. Methods: Multivariable-adjusted Cox proportional hazard models were used to estimate the association between blood mtDNA-CN and incident type 2 diabetes in 285,967 unrelated European individuals from UK Biobank free of type 2 diabetes at baseline. Additionally, a cross-sectional analysis was performed to investigate the association between blood mtDNA-CN and BMI. We also assessed the potentially causal relationship between blood mtDNA-CN and type 2 diabetes (N=898,130 from DIAGRAM, N=215,654 from FinnGen) and BMI (N=681,275 from GIANT) using bi-directional two-sample MR. Results: During a median follow-up of 11.87 years, 15,111 participants developed type 2 diabetes. Participants with a higher level of blood mtDNA-CN are at lower risk of developing type 2 diabetes (HR 0.90 [95% CI 0.89, 0.92]). After additional adjustment for BMI and other confounders, these results attenuated moderately and remained present. The multivariable-adjusted cross-sectional analyses showed that higher blood mtDNA-CN was associated with lower BMI (-0.12 [95% CI -0.14, -0.10]) kg/m Conclusions/interpretation: The results from the present study indicate that the observed association between low blood mtDNA-CN and higher risk of type 2 diabetes is likely not causal.
MeSH term(s)	Cross-Sectional Studies ; DNA Copy Number Variations/genetics ; DNA, Mitochondrial/genetics ; Diabetes Mellitus, Type 2/genetics ; Humans ; Mitochondria ; Prospective Studies
Chemical Substances	DNA, Mitochondrial
Language	English
Publishing date	2022-07-22
Publishing country	Germany
Document type	Journal Article
ZDB-ID	1694-9
ISSN	1432-0428 ; 0012-186X
ISSN (online)	1432-0428
ISSN	0012-186X
DOI	10.1007/s00125-022-05759-6
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Article ; Online: CONQUER: an interactive toolbox to understand functional consequences of GWAS hits.

Bouland, Gerard A / Beulens, Joline W J / Nap, Joey / van der Slik, Arno R / Zaldumbide, Arnaud / 't Hart, Leen M / Slieker, Roderick C

NAR genomics and bioinformatics

2020 Volume 2, Issue 4, Page(s) lqaa085

Abstract: Numerous large genome-wide association studies have been performed to understand the influence of genetics on traits. Many identified risk loci are in non-coding and intergenic regions, which complicates understanding how genes and their downstream ... ...

Abstract	Numerous large genome-wide association studies have been performed to understand the influence of genetics on traits. Many identified risk loci are in non-coding and intergenic regions, which complicates understanding how genes and their downstream pathways are influenced. An integrative data approach is required to understand the mechanism and consequences of identified risk loci. Here, we developed the R-package CONQUER. Data for SNPs of interest are acquired from static- and dynamic repositories (build GRCh38/hg38), including GTExPortal, Epigenomics Project, 4D genome database and genome browsers. All visualizations are fully interactive so that the user can immediately access the underlying data. CONQUER is a user-friendly tool to perform an integrative approach on multiple SNPs where risk loci are not seen as individual risk factors but rather as a network of risk factors.
Language	English
Publishing date	2020-10-27
Publishing country	England
Document type	Journal Article
ISSN	2631-9268
ISSN (online)	2631-9268
DOI	10.1093/nargab/lqaa085
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